Academic literature on the topic 'Fault decomposition'
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Journal articles on the topic "Fault decomposition"
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Gupta, Abhishek, and Ramesh Kumar Pachar. "A Hybrid Signal Processing Technique for Identification and Categorization of Faults in IEEE-9 Bus System." Advanced Engineering Forum 49 (May 31, 2023): 43–55. http://dx.doi.org/10.4028/p-jkw3p9.
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A hybrid signal processing technique (HSPT) is proposed in this manuscript for identification and categorization of faults in electrical transmission network. A fault indicator (FI) is suggested by decomposition of the currents by application of Alienation coefficient (ACF), Stockwell transform (ST) and Hilbert transform (HT) for identification of faults. An indicator for ground involvement during faulty condition (SGFI) is being suggested to detect the type of fault. The categorization of faults is done by utilizing faulty phase numbers and SGFI. It is found that the proposed technique is effective in identification of faults and to classify them in different scenarios together with fault on A-phase to ground (AGF), double phase fault (ABF), fault on two phases and ground (ABGF), three phase fault (ABCF) and three phase fault including ground (ABCGF). Study is done and validated on IEEE-9 bus system using MATLAB/Simulink environment. The effectiveness and applicability of the proposed technique with respect to different parameters of faults such as Fault Incidence Angle, Fault Impedance, Line loading, Generator Supply and Noise level is also checked. The results shows that proposed scheme is able to detect and classify the faults in different faulty events.
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Manjunatha, G., and H. C. Chittappa. "Bearing Fault Classification using Empirical Mode Decomposition and Machine Learning Approach." Journal of Mines, Metals and Fuels 70, no. 4 (2022): 214. http://dx.doi.org/10.18311/jmmf/2022/30060.
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Industrial machinery often breakdowns due to faults in rolling bearing. Bearing diagnosis plays a vital role in condition monitoring of machinery. Operating conditions and working environment of bearings make them prone to single or multiple faults. In this research, signals from both healthy and faulty bearings are extracted and decomposed into empirical modes. By analyzing different empirical modes from 8 derived empirical modes for healthy and faulty bearings under different fault sizes, the first mode has the most information to classify bearing condition. From the first empirical mode eight features in time domain were calculated for various bearing conditions like healthy, rolling element fault, outer and inner race fault. The feature extraction of vibration signal based on Empirical Mode Decomposition (EMD) is extensively explored and applied in diagnosis of fault in rolling bearings. This paper presents mathematical analysis for selection of valid Intrinsic Mode Functions (IMFs) of EMD. These chosen features are trained and classified using different classifiers. Among them K-star classifier is most reliable to categorize the bearing defects.
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Fang, Liang, and Hongchun Sun. "Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery." Applied Sciences 8, no. 9 (2018): 1441. http://dx.doi.org/10.3390/app8091441.
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A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode decomposition (EMD), variational mode decomposition (VMD) and EEMD, the better performance of this method is proven with three experimental analyses of faulty gear, a faulty rolling bearing and a faulty shaft. The results demonstrate that the proposed method is efficient to extract the fault features of single-channel vibration signals of rotating machinery.
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Zhang, Dingcheng, Dejie Yu, and Xing Li. "Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 24 (2016): 4670–83. http://dx.doi.org/10.1177/0954406216671542.
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The fault diagnosis of rotating machinery is quite important for the security and reliability of the overall mechanical equipment. As the main components in rotating machinery, the gear and the bearing are the most vulnerable to faults. In actual working conditions, there are two common types of faults in rotating machinery: the single fault and the compound fault. However, both of them are difficult to detect in the incipient stage because the weak fault characteristic signals are usually submerged by strong background noise, thus increasing the difficulty of the weak fault feature extraction. In this paper, a novel decomposition method, optimal resonance-based signal spares decomposition, is applied for the detection of those two types of faults in the rotating machinery. This method is based on the resonance-based signal spares decomposition, which can nonlinearly decompose vibration signals of rotating machinery into the high and the low resonance components. To extract the weak fault characteristic signals in the presence of strong noise effectively, the genetic algorithm is used to obtain the optimal decomposition parameters. Then, the optimal high and low resonance components, which include the fault characteristic signals of rotating machinery, can be obtained by using the resonance-based signal spares decomposition method with the optimal decomposition parameters. Finally, the high and the low resonance components are subject to the Hilbert transform demodulation analysis; the faults of rotating machinery can be diagnosed based on the obtained envelop spectra. The optimal resonance-based signal spares decomposition method is successfully applied to the analysis of the simulation and experiment vibration signals. The analysis results demonstrate that the proposed method can successfully extract the fault features in rotating machinery.
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Tong, Shuiguang, Yidong Zhang, Jian Xu, and Feiyun Cong. "Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 12 (2017): 2280–96. http://dx.doi.org/10.1177/0954406217715483.
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In rotating machinery, the malfunctions of rolling bearings are one of the most common faults. To prevent machine breakdown, the pattern recognition of rolling bearing faults has been a pivotal issue for fault identification and classification. This study proposes a new feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) for fault classification. The proposed E–S method (EEMD combined with SVD using feature parameters) intends to enhance the faults identification capability in different working conditions, including various fault types (FT), fault severities (FS), and fault loads (FL). In this study, the E–S method is adopted to analyze the simulated signals. And the experiment further discusses three cases of different FT, FS, and FL separately under six different classifiers. The experimental results show that different fault classes can be effectively distinguished by the proposed E–S in comparison with other traditional feature extraction methods. Hence, the proposed method is verified to have an effective and excellent performance in bearing fault classification.
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Jing, Liuming, Lei Xia, Tong Zhao, and Jinghua Zhou. "An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition." Applied Sciences 13, no. 16 (2023): 9132. http://dx.doi.org/10.3390/app13169132.
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The influence of the control strategy of the power electronic converter obscures the fault characteristics of DC distribution networks. The existence of arc faults over an extended period of time poses a grave threat to the security of power grids and may result in electric shock, fire, and other catastrophes. In recent years, the method of fault localization based on the traveling wave method has been a popular topic of research in the field of DC distribution system protection. In this paper, the fault localization principle of the traveling wave method is described in depth, and the propagation characteristics of the traveling wave of fault current in the online mode network are deduced. We present a method for wave head calibration that combines empirical mode decomposition (EMD) and singular value decomposition (VMD). After the fault-traveling current signal has been subjected to EMD, the first eigenmode function is extracted and subjected to singular value decomposition (SVD). After SVD, the detail component can reflect the singularity of the signal. The point of the maximum value of the detail component signal corresponds to the moment when the faulty traveling wave head reaches the monitoring point. Finally, the DC distribution system is modeled based on the PSCAD/EMTDC simulation environment, and the fault location method is verified. The simulation results show that the method can effectively realize fault localization.
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Hu, Pan, Cunsheng Zhao, Jicheng Huang, and Tingxin Song. "Intelligent and Small Samples Gear Fault Detection Based on Wavelet Analysis and Improved CNN." Processes 11, no. 10 (2023): 2969. http://dx.doi.org/10.3390/pr11102969.
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Traditional methods for identifying gear faults typically require a substantial number of faulty samples, which in reality are challenging to obtain. To tackle this challenge, this paper introduces a sophisticated approach for intelligent gear fault identification, utilizing discrete wavelet decomposition and an enhanced convolutional neural network (CNN) optimized for scenarios with limited sample data. Initially, the features of the sample signal are extracted and enhanced using discrete wavelet decomposition. Subsequently, the refined signal is transformed into a two-dimensional image through a Markov transition field, preparing it for improved two-dimensional CNN training. Finally, the refined network model is applied to assess the gear fault dataset, achieving a training accuracy of 97% and a classification accuracy of 88.33%. This demonstrates the method’s feasibility and effectiveness in identifying gear faults with limited sample data.
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Liao, Zhiqiang, Xuewei Song, Baozhu Jia, and Peng Chen. "Automatic Bearing Fault Feature Extraction Method via PFDIC and DBAS." Mathematical Problems in Engineering 2021 (May 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/6655081.
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Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, this work presents a singular value decomposition-based automatic fault feature extraction method that uses the probability-frequency density information criterion (PFDIC) and dual beetle antennae search (DBAS). DBAS employs embedded dimension and singular values as dynamic variables and PFDIC as a two-stage objective to optimize the best parameters. The optimization results work for singular value decomposition for bearing fault feature extraction. The extracted fault signals combined with envelope demodulation can efficiently diagnose bearing faults. The superiority and applicability of the proposed method are validated by simulation signals, engineering signals, and comparison experiments. Results demonstrate that the proposed method can sufficiently extract fault features and accurately diagnose faults.
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Dou, Chun Hong. "Fault Feature Extraction for Gearboxes Using Empirical Mode Decomposition." Advanced Materials Research 383-390 (November 2011): 1376–80. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1376.
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The paper uses empirical mode decomposition to extract the fault feature of gearboxes. Traditional techniques fail to process the non-stationary and nonlinear signals. Empirical mode decomposition is a powerful tool for the non-stationary and nonlinear signal analysis and has attracted considerable attention recently. First, a simulation signal is used to measure the performance of the empirical mode decomposition method. Then, the empirical mode decomposition method is applied to analyze the signals captured from the gearbox with multiple faults and successfully extracts the multiple fault information from the collected signals. The results show that empirical mode decomposition could be a helpful method for mechanical fault feature extraction.
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Zhao, Nanyang, Zhiwei Mao, Donghai Wei, Haipeng Zhao, Jinjie Zhang, and Zhinong Jiang. "Fault Diagnosis of Diesel Engine Valve Clearance Based on Variational Mode Decomposition and Random Forest." Applied Sciences 10, no. 3 (2020): 1124. http://dx.doi.org/10.3390/app10031124.
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Diesel engines, as power equipment, are widely used in the fields of the automobile industry, ship industry, and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in the time domain and angular domain, on which the current diagnosis methods are based, are easily affected by working conditions or are hard to extract accurate enough from, as the diesel engine keeps running in transient and non-stationary processes. This work aimed at diagnosing this fault mainly based on frequency band features, which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively, a decomposition technique based on improved variational mode decomposition was investigated in this work. As the connection between the features and the fault was fuzzy, the random forest algorithm was used to analyze the correspondence between features and faults. In addition, the feature dimension was reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition showed that the method based on variational mode decomposition and random forest was capable to detect the valve clearance fault effectively.
Dissertations / Theses on the topic "Fault decomposition"
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Arkan, Muslum. "Stator fault diagnosis in induction motors." Thesis, University of Sussex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310244.
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Needham, Donald Michael. "A formal approach to hazard decomposition in Software Fault Tree Analysis." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/28230.
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As digital control systems are used in life-critical applications, assessment of the safety of these control systems becomes increasingly important. One means of formally performing this assessment is through fault tree analysis. Software Fault Tree Analysis (SFTA) starts with a system-level hazard that must be decomposed in a largely-human-intensive manner until specific modules of the software system are indicated. These modules can then be formally analyzed using statement templates. The focus of this thesis is to approach the decomposition of a system-level hazard from a formalized standpoint. Decomposition primarily proceeds along two distinct but interdependent dimensions, specificity of event and subsystem size. The Specificity-of-Event dimension breaks abstract or combined events into the specific system events that must be analyzed by the fault tree. The Subsystem-Size dimension deals with the scope of the hazard, and itemizes the subsystems where localized events may lead to the hazard. Decomposition templates are developed in this thesis to provide a framework for decomposing a system-level hazard to the point at which line-by-line code analysis can be conducted with existing statement templates. These templates serve as guides for conducting the decomposition, and ensure that as many as possible of all the applicable decomposition aspects are evaluated
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Ye, Fanchao. "Fault decomposition characteristics and application feasibility assessment of C4F7N-CO2-O2 mixed insulating gas." Electronic Thesis or Diss., Orléans, 2023. http://www.theses.fr/2023ORLE1030.
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Dans ce travail de doctorat, une étude théorique et expérimentale systématique a été menée sur l'isolation du mélange de gaz C4F7N-CO2-O2 respectueux de l'environnement et ses caractéristiques de décomposition et de biosécurité sous défauts électriques et thermiques. Sur la base de la méthode de dynamique moléculaire ReaxFF, le processus de décomposition thermique du mélange gazeux sous différentes teneurs en O2 et températures est simulé. En combinant les résultats simulés avec des essais de décomposition thermique, le processus cinétique de décomposition thermique du mélange gazeux et le mécanisme d'évolution de ses sous-produits dans différentes conditions sont révélés. En même temps, le mécanisme d'influence de la teneur en O2 sur la tension de claquage et les valeurs caractéristiques statistiques du mélange C4F7N-CO2-O2 pour des décharges partielles sont analysés et le mécanisme d'influence de différents facteurs sur la génération et l'inhibition des gaz et des sous-produits solides au cours du processus de décomposition par décharge du mélange gazeux est clarifié. En conclusion, sur la base des résultats de simulation et expérimentaux, nous proposons la quantité optimale d'additif O2 et les composants caractéristiques de diagnostic du mélange gazeux C4F7N-CO2-O2 pour les défauts des équipements isolés au gaz moyenne tension ; nous testons la biosécurité du C4F7N et de ses produits de décomposition après l'exposition à l'arc, puis évaluons la faisabilité, la sécurité de l'application du mélange de gaz C4F7N-CO2-O2 dans l'équipement en combinant avec les caractéristiques isolantes et de décomposition électrique et thermique du mélange de gaz C4F7N-CO2-O2 et, les résultats de la biosécurité<br>In this doctoral work, a systematic theoretical and experimental study has been carried out on the insulation of environmentally friendly C4F7N-CO2-O2 gas mixture and on its decomposition characteristics and biosafety under electrical and thermal faults. Based on the ReaxFF molecular dynamics method, the thermal decomposition process of the gas mixture under different O2 contents and temperatures is simulated. The kinetic process of the thermal decomposition of the gas mixture and the evolution mechanism of its by-products under different conditions are revealed by combining with thermal decomposition tests. Meanwhile, the influence mechanism of O2 content on the breakdown voltage and partial discharge statistical characteristic values of the C4F7N-CO2-O2 mixture is analyzed, and the influence mechanism of different factors on the generation and inhibition of gas and solid by-products during the discharge decomposition process of the gas mixture is clarified. In conclusion, based on the simulation and experimental results, we propose the optimal O2 additive amount and fault diagnosis characteristic components of C4F7N-CO2-O2 gas mixture for medium-voltage gas-insulated equipmentwe test the biosafety of C4F7N and its arc decomposition products, and then evaluate the feasibility and safety of applying C4F7N-CO2-O2 gas mixture in equipment by combining with the insulating and electrical and thermal decomposition characteristics of C4F7N-CO2-O2 gas mixture and the results of the biosafety
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BUZZONI, Marco. "Development and validation of Blind Deconvolution and Empirical Mode Decomposition techniques for impulsive fault diagnosis in rotating machines." Doctoral thesis, Università degli studi di Ferrara, 2018. http://hdl.handle.net/11392/2478776.
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La diagnosi di difetti in macchine rotanti basata sull’analisi vibrazionale ha raggiunto una soddisfacente fase di maturità, essendo disponibili numerose metodologie consolidate per la rilevazione e l’identificazione di difetti. Tuttavia, diverse problematiche restano ancora aperte; questa tesi ne prende in considerazione due. Da un lato, la ciclostazionarietà non è stata ancora utilizzata esplicitamente per progettare criteri di deconvoluzione cieca per la diagnosi di macchine rotanti, sebbene l'importanza di applicare la ciclostazionarietà per scopi diagnostici sia stata ampiamente riconosciuta. Dall’altro, la localizzazione di un difetto che si verifica in una ruota dentata installata in un albero intermedio di un riduttore a più stadi, particolarmente complessa per la sovrapposizione di più sorgenti di vibrazione, non è stata ancora oggetto di studi. In questo contesto, basandosi sulla teoria dei processi ciclostazionari, la tesi affronta questi due aspetti, differenti ma correlati e complementari, relativi all'identificazione di difetti localizzati in ingranaggi e cuscinetti volventi.
La prima parte della tesi propone un metodo di deconvoluzione cieca, basato sul quoziente di Rayleigh generalizzato, risolto mediante un algoritmo iterativo di decomposizione agli autovalori. Questo approccio è caratterizzato dalla presenza di una matrice di pesatura che guida il processo di deconvoluzione, grazie alla quale il metodo può essere facilmente adattato a criteri arbitrari. Un nuovo criterio basato sulla massimizzazione della ciclostazionarietà del secondo ordine viene proposto e confrontato con altri metodi di deconvoluzione cieca esistenti in letteratura. Il confronto, effettuato su segnali simulati e segnali sperimentali, ha dimostrato che l’algoritmo è efficace nella stima delle eccitazioni ciclostazionarie a partire da risposte vibratorie sia a regimi stazionari sia a regimi non stazionari. Questo metodo è validato attraverso due diversi casi sperimentali relativi ad un rotismo ordinario a due stadi e ad un cuscinetto volvente. L'originalità di questa parte riguarda l'introduzione di un nuovo algoritmo di deconvoluzione cieca basato su di un criterio ciclostazionario che consente l'estrazione di sorgenti ciclsotazionarie aventi una determinata frequenza ciclica. Sulla base di questo metodo, sono proposti inoltre due procedure originali per la diagnosi di cuscinetti e ingranaggi. In particolare, queste procedure si basano sul criterio ciclostazionario massimizzato mediante il metodo di deconvoluzione cieca che consente la diagnosi del difetto in termini di tipologia e di severità.
La seconda parte riguarda lo sviluppo e la validazione di un metodo per l'identificazione di difetti localizzati presenti in una ruota dentata calettata su un albero intermedio di un rotismo ordinario multi-stadio. In questo contesto, si propone una metodologia che combina la Empirical Mode Decomposition e la media sincrona per separare il segnale ciclostazionario del primo ordine relativo alle ruote dentate sincrone, montate sul medesimo albero, in un insieme di segnali rappresentativi relativi alle singole ruote dentate. I modi oscillatori fisicamente significativi sono selezionati attraverso un criterio basato sui coefficienti di correlazione di Pearson. Il rilevamento dei guasti viene eseguito successivamente mediante indicatori di condizione dedicati. In aggiunta agli indicatori di condizione standard, sono proposti due nuovi indicatori di condizione sensibili alle variazioni di energia del segnale sul passo della ruota, che si sono dimostrati particolarmente efficaci per il rilevamento dei difetti localizzati. L’efficacia della metodologia proposta è esaurientemente discussa mediante l’applicazione a segnali simulati e da due set di dati sperimentali. In tutti i casi esaminati, i risultati mostrano la capacità di identificare con successo la ruota difettosa nei casi di più ruote calettate sullo stesso albero.<br>Vibration analysis provides a useful aid for monitoring many mechanical systems and industrial processes. In recent years, the vibration-based diagnosis of machines and mechanical systems has reached a satisfactory stage of maturity. Several established signal processing methodologies are now available for detecting and identifying localized faults, especially for gears and bearings. However, several questions are still open. Among them, this thesis addresses two correlated issues. On the one hand, cyclostationarity has not been explicitly used to design blind deconvolution criteria for machine diagnosis before now, although the importance to take advantage of cyclostationarity for diagnostics purpose has been widely recognized. Concurrently, the localization of a gear fault occurring in a gear located in an intermediate shaft of a multi-stage gearbox can be particularly complex due to the superposition of vibration signatures of different synchronous wheels. Nevertheless, this issue has not been investigated yet. On these grounds, this thesis has been focused on these two different but complementary facets about impulsive fault identification in rotating machines both rooted in the cyclostationary framework.
The first part of the thesis focuses on a blind deconvolution method based on the generalized Rayleigh quotient and solved by means of an iterative eigenvalue decomposition algorithm. This approach is characterized by the presence of a weighting matrix that drives the deconvolution process, whereby it can be easily adapted to arbitrary criteria. A novel criterion based on the maximization of the cyclostationarity of the signal is proposed and compared with the other blind deconvolution methods existing in the literature. The proposed algorithm is extensively compared taking into account cyclostationary synthetic signals and real ones, demonstrating superior capability to recover cyclostationary sources both in stationary regimes and non-stationary regimes. This method is successfully validated for diagnostic purposes through two different experimental cases consisting of a gear tooth spall and an outer race bearing fault. The originality of this part mainly regards the introduction of a novel blind deconvolution algorithm based on a cyclostationary criterion that allows for the extraction of cyclostationary sources having a given cyclic frequency. Two original and consistent diagnostic protocols for bearing and gear diagnosis are proposed as well. In particular, these diagnostic procedures take advantage of the maximized cyclostationary criterion computed by way of the proposed blind deconvolution method allowing the diagnosis in terms of fault type and severity.
The second part addresses a method for the identification of gear tooth faults occurring in a wheel located in the intermediate shaft of multi-stage gearboxes. In this context, this part introduces a methodology which combines the Empirical Mode Decomposition and the Time Synchronous Average in order to separate the first-order cyclostationary signal of the synchronous gears mounted on the same shaft into a set of representing signals of the single gears. The physical meaningful modes are selected by means of a criterion based on Pearson’s correlation coefficients and the fault detection is performed by dedicated condition indicators. The proposed methodology is exhaustively discussed and supported by simulated examples as well as two experimental datasets. This original strategy successfully identifies the faulty gear in both the experimental tests and therefore can be considered reliable for the identification of a faulty gear when the fault occurs in a shaft with multiple gears. Furthermore, two novel condition indicators sensitive to signal energy variations on the circular pitch are proposed and proved to be effective for the local gear fault detection.
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KEHLENBACH, JOSUA. "Fault diagnosis of axlebox roller bearings of high speed rail vehicles based on empirical mode decomposition and machine learning." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299774.
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Axlebox bearings are one of the most critical components of a rail vehicle with regard to safety. An axlebox bearing that breaks during operation can be dangerous for the passengers and expensive for the operator. In-service failure of axlebox bearings has been the cause of many catastrophic accidents. Thus, it is of utmost importance to predict bearing failures as early as possible. This will increase reliability and safety of the vehicle as well as reduce the vehicle maintenance cost. Monitoring of roller bearings is an active research eld, and many methods have been proposed by other researchers. Many of these methods employ complex algorithms to make the most use of the given measurements. The algorithms often lack interpretability and have high computational costs, making them dicult to employ in an on-board system. This thesis proposes an interpretable and transparent algorithm that predicts bearing damages with high accuracy. Meanwhile, it tries to retain interpretability as much as possible. The algorithm is based on Empirical Mode Decomposition (EMD) and Singular Value Decomposition (SVD). These two techniques extract essential and meaningful information from the axlebox accelerations. The algorithm is benchmarked on two benchmark datasets, and the results are compared to the respective literature. Then the algorithm is employed on the railway axlebox acceleration measurements that were taken on an axlebox test bench available at SWJTU. The proposed algorithm can be extended to incorporate additional measurements of dierent types, e.g. sound or temperature measurements. The incorporation of other types of measurements will improve the performance of the algorithm even further.<br>Axelbox lager är en av de viktigaste komponenterna i ett järnvägsfordon när det berör säkerheten. Ett axelbox lager som havererar under drift kan vara farligt for passagerarna och även dyrt för operatören. Driftfel av lagren har varit orsaken till många katastrofala olyckor. Därför är det av yttersta vikt att förutsäga lagerfel så tidigt som möjligt. Detta ökar fordonets tillförlitlighet och säkerhet samt minskar underhållskostnaderna. Mycket forskning har utförts inom övervakning av rullager. Många metoder använder komplexa algoritmer för att maximalt utnyttja matningarna. Algoritmerna saknar ofta tolkbarhet och har höga beräkningskostnader, vilket gör dem svåra att använda i ett integrerat system. Denna avhandling kombinerar era metoder för databehandling och maskininlärning till en algoritm som kan förutsäga lagerskador med hög precision, samtidigt som tolkningsförmågan bibehalls. Bland andra välkända metoder sa använder algoritmen Empirical Mode Decomposition (EMD) och Singular Value Decomposition (SVD) för att extrahera väsentlig information for vibrationsmätningarna. Algoritmen testas sedan med tre olika vibrationsdatamängder, varav en mättes specikt med tanke på simulering av axelbox lager. Ett annat mål med algoritmen är att göra den tillämpad för ytterligare mätningar. Det bör vara möjligt att inkludera mätningar av olika slag, dvs ljud- eller temperaturmätningar, och därigenom förbättra resultaten. Detta skulle minska implementeringskostnaden avsevärt eftersom befintliga sensorer används för detta ändamål. I händelsen av att de föreslagna metoderna inte fungerar med nya mätningar är det även möjligt att integrera ytterligare funktioner i algoritmen.
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Kroenke, Samantha E. "A Study of the Herald-Phillipstown Fault in the Wabash Valley using Drillhole and 3-D Seismic Reflection Data." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/676.
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In June 2009, a 2.2 square mile 3-D high resolution seismic reflection survey was shot in southeastern Illinois in the Phillipstown Consolidated oilfield. A well was drilled in the 3-D survey area to tie the seismic to the geological data with a synthetic seismogram from the sonic log. The objectives of the 3-D seismic survey were three-fold: 1.) To image and interpret faulting of the Herald-Phillipstown Fault using drillhole-based geological and seismic cross-sections and structural contour maps created from the drillhole data and seismic reflection data, 2.) To test the effectiveness of imaging the faults by selected seismic attributes, and 3.) To compare spectral decomposition amplitude maps with an isochron map and an isopach map of a selected geologic interval (VTG interval). Drillhole and seismic reflection data show that various formation offsets increase near the main Herald-Phillipstown fault, and that the fault and its large offset subsidiary faults penetrate the Precambrian crystalline basement. A broad, northeast-trending 10,000 feet wide graben is consistently observed in the drillhole data. Both shallow and deep formations in the geological cross-sections reveal small horst and graben features within the broad graben created possibly in response to fault reactivations. The HPF faults have been interpreted as originally Precambrian age high-angle, normal faults reactivated with various amounts and types of offset. Evidence for strike-slip movement is also clear on several faults. Changes in the seismic attribute values in the selected interval and along various time slices throughout the whole dataset correlate with the Herald-Phillipstown faults. Overall, seismic attributes could provide a means of mapping large offset faults in areas with limited or absent drillhole data. Results of the spectral decomposition suggest that if the interval velocity is known for a particular formation or interval, high-resolution 3-D seismic reflection surveys could utilize these amplitudes as an alternative seismic interpretation method for estimating formation thicknesses. A VTG isopach map was compared with an isochron map and a spectral decomposition amplitude map. The results reveal that the isochron map strongly correlates with the isopach map as well as the spectral decomposition map. It was also found that thicker areas in the isopach correlated with higher amplitude values in the spectral decomposition amplitude map. Offsets along the faults appear sharper in these amplitudes and isochron maps than in the isopach map, possibly as a result of increased spatial sampling.
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Maree, J. P. (Johannes Philippus). "Fault detection for the Benfield process using a closed-loop subspace re-identification approach." Diss., University of Pretoria, 2008. http://hdl.handle.net/2263/29844.
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Closed-loop system identification and fault detection and isolation are the two fundamental building blocks of process monitoring. Efficient and accurate process monitoring increases plant availability and utilisation. This dissertation investigates a subspace system identification and fault detection methodology for the Benfield process, used by Sasol, Synfuels in Secunda, South Africa, to remove CO2 from CO2-rich tail gas. Subspace identification methods originated between system theory, geometry and numerical linear algebra which makes it a computationally efficient tool to estimate system parameters. Subspace identification methods are classified as Black-Box identification techniques, where it does not rely on a-priori process information and estimates the process model structure and order automatically. Typical subspace identification algorithms use non-parsimonious model formulation, with extra terms in the model that appear to be non-causal (stochastic noise components). These extra terms are included to conveniently perform subspace projection, but are the cause for inflated variance in the estimates, and partially responsible for the loss of closed-loop identifiably. The subspace identification methodology proposed in this dissertation incorporates two successive LQ decompositions to remove stochastic components and obtain state-space models of the plant respectively. The stability of the identified plant is further guaranteed by using the shift invariant property of the extended observability matrix by appending the shifted extended observability matrix by a block of zeros. It is shown that the spectral radius of the identified system matrices all lies within a unit boundary, when the system matrices are derived from the newly appended extended observability matrix. The proposed subspace identification methodology is validated and verified by re-identifying the Benfield process operating in closed-loop, with an RMPCT controller, using measured closed-loop process data. Models that have been identified from data measured from the Benfield process operating in closed-loop with an RMPCT controller produced validation data fits of 65% and higher. From residual analysis results, it was concluded that the proposed subspace identification method produce models that are accurate in predicting future outputs and represent a wide variety of process inputs. A parametric fault detection methodology is proposed that monitors the estimated system parameters as identified from the subspace identification methodology. The fault detection methodology is based on the monitoring of parameter discrepancies, where sporadic parameter deviations will be detected as faults. Extended Kalman filter theory is implemented to estimate system parameters, instead of system states, as new process data becomes readily available. The extended Kalman filter needs accurate initial parameter estimates and is thus periodically updated by the subspace identification methodology, as a new set of more accurate parameters have been identified. The proposed fault detection methodology is validated and verified by monitoring process behaviour of the Benfield process. Faults that were monitored for, and detected include foaming, flooding and sensor faults. Initial process parameters as identified from the subspace method can be tracked efficiently by using an extended Kalman filter. This enables the fault detection methodology to identify process parameter deviations, with a process parameter deviation sensitivity of 2% or higher. This means that a 2% parameter deviation will be detected which greatly enhances the fault detection efficiency and sensitivity.<br>Dissertation (MEng)--University of Pretoria, 2008.<br>Electrical, Electronic and Computer Engineering<br>unrestricted
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Abboud, Layane. "Time Reversal techniques applied to wire fault detection and location in wire networks." Phd thesis, Supélec, 2012. http://tel.archives-ouvertes.fr/tel-00771964.
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In this thesis we present new approaches in the domains of soft fault detection and location in complex wire networks, based on the properties of time reversal. When addressing the detection of soft faults, the idea is to adapt the testing signal to the network under test, instead of being predefined for all the tested networks, as opposed to standard reflectometry techniques. We prove that this approach, which we name the Matched Pulse approach (MP), is beneficial whenever the system is more complex, i.e., its response is richer in echoes, which is opposed to common understanding. The MP analysis is conducted via a formal mathematical analysis, followed by simulation and experimental results validating the proposed approach. In the domain of soft fault location, and based on the DORT (Décomposition de l'Opérateur de Retournement Temporel) properties, we derive a distributive non-iterative method able to synthesize signals that focus on the fault position. Through a statistical study we analyze some of the influencing parameters on the performance of the method, and then simulation and experimental results show that the method is able to synthesize signals directly focalizing on the soft fault position, without the need for iterations.
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Picchi, Daniel da Costa. "Avaliação da técnica de decomposição por componentes ortogonais para identificação de faltas de alta impedância." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-13122018-134842/.
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Este trabalho apresenta o estado da arte das técnicas mais aplicadas para localização de faltas e modelagem de faltas de alta impedância e propõe a utilização de uma recente técnica baseada na decomposição dos sinais em componentes ortogonais. Este estudo avalia a aplicabilidade da técnica proposta utilizando dados reais de um sistema de distribuição de energia brasileiro, além de apresentar os conceitos teóricos sobre a decomposição em componentes ortogonais.<br>This work presents the state of the art of the most used techniques for locating and modelling high impedance faults and proposes the use of a recent technique based on the decomposition of the signals in orthogonal components. The objective of this study is to evaluate the application of the proposed technique using real data from a Brazilian distribution network, and presents the theory on orthogonal decomposition.
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Jalboub, Mohamed K. "Investigation of the application of UPFC controllers for weak bus systems subjected to fault conditions. An investigation of the behaviour of a UPFC controller: the voltage stability and power transfer capability of the network and the effect of the position of unsymmetrical fault conditions." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5699.
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In order to identify the weakest bus in a power system so that the Unified Power Flow Controller could be connected, an investigation of static and dynamic voltage stability is presented. Two stability indices, static and dynamic, have been proposed in the thesis. Multi-Input Multi-Output (MIMO) analysis has been used for the dynamic stability analysis. Results based on the Western System Coordinate Council (WSCC) 3-machine, 9-bus test system and IEEE 14 bus Reliability Test System (RTS) shows that these indices detect with the degree of accuracy the weakest bus, the weakest line and the voltage stability margin in the test system before suffering from voltage collapse.
Recently, Flexible Alternating Current Transmission systems (FACTs) have become significant due to the need to strengthen existing power systems. The UPFC has been identified in literature as the most comprehensive and complex FACTs equipment that has emerged for the control and optimization of power flow in AC transmission systems. Significant research has been done on the UPFC. However, the extent of UPFC capability, connected to the weakest bus in maintaining the power flows under fault conditions, not only in the line where it is installed, but also in adjacent parallel lines, remains to be studied. In the literature, it has normally been assumed the UPFC is disconnected during a fault period. In this investigation it has been shown that fault conditions can affect the UPFC significantly, even if it occurred on far buses of the power system. This forms the main contribution presented in this thesis. The impact of UPFC in minimizing the disturbances in voltages, currents and power flows under fault conditions are investigated. The WSCC 3-machine, 9-bus test system is used to investigate the effect of an unsymmetrical fault type and position on the operation of UPFC controller in accordance to the G59 protection, stability and regulation. Results show that it is necessary to disconnect the UPFC controller from the power system during unsymmetrical fault conditions.<br>Libyan Government
Books on the topic "Fault decomposition"
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Needham, Donald Michael. A formal approach to hazard decomposition in Software Fault Tree Analysis. Naval Postgraduate School, 1990.
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McCullers, William T. III. Probabilistic analysis of fault trees using pivotal decomposition. 1985.
Find full textBook chapters on the topic "Fault decomposition"
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Breedveld, Peter C. "Decomposition of Multiports." In Bond Graphs for Modelling, Control and Fault Diagnosis of Engineering Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47434-2_1.
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Ragot, José, Didier Maquin, and Frédéric Kratz. "Observability and Redundancy Decomposition Application to Diagnosis." In Issues of Fault Diagnosis for Dynamic Systems. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-3644-6_3.
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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Statistics Decomposition and Monitoring in Original Variable Space." In Intelligent Control and Learning Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_6.
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AbstractThe traditional process monitoring method first projects the measured process data into the principle component subspace (PCS) and the residual subspace (RS), then calculates $$\mathrm T^2$$ T 2 and $$\mathrm SPE$$ S P E statistics to detect the abnormality. However, the abnormality by these two statistics are detected from the principle components of the process. Principle components actually have no specific physical meaning, and do not contribute directly to identify the fault variable and its root cause. Researchers have proposed many methods to identify the fault variable accurately based on the projection space. The most popular is contribution plot which measures the contribution of each process variable to the principal element (Wang et al. 2017; Luo et al. 2017; Liu and Chen 2014). Moreover, in order to determine the control limits of the two statistics, their probability distributions should be estimated or assumed as specific one. The fault identification by statistics is not intuitive enough to directly reflect the role and trend of each variable when the process changes.
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Daigle, Matthew J., Anibal Bregon, and Indranil Roychoudhury. "Diagnosis of Hybrid Systems Using Structural Model Decomposition." In Fault Diagnosis of Hybrid Dynamic and Complex Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74014-0_8.
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Chanthery, Elodie, Anna Sztyber, Louise Travé-Massuyès, and Carlos Gustavo Pérez-Zuñiga. "Process Decomposition and Test Selection for Distributed Fault Diagnosis." In Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55789-8_78.
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Maslennikow, Oleg, Juri Kaniewski, and Roman Wyrzykowski. "Fault tolerant QR-decomposition algorithm and its parallel implementation." In Euro-Par’98 Parallel Processing. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0057933.
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Xing, J. P., and T. R. Lin. "Bearing Fault Diagnosis Based on the Variational Mode Decomposition Technique." In Engineering Assets and Public Infrastructures in the Age of Digitalization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48021-9_75.
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Kim, J. H., and S. M. Reddy. "Fault-Tolerant LU-Decomposition in a Two-Dimensional Systolic Array." In Concurrent Computations. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-5511-3_29.
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Lin, Jinshan. "Fault Feature Extraction of Gearboxes Using Ensemble Empirical Mode Decomposition." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23214-5_63.
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Lei, Yaguo. "Fault Diagnosis of Rotating Machinery Based on Empirical Mode Decomposition." In Smart Sensors, Measurement and Instrumentation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56126-4_10.
Full textConference papers on the topic "Fault decomposition"
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Fan, Xianfeng, and Ming J. Zuo. "Gearbox Fault Detection Using Empirical Mode Decomposition." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59349.
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Local faults in a gearbox cause impacts and the collected vibration signal is often non-stationary. Identification of impulses within the non-stationary vibration signal is key to fault detection. Recently, the technique of Empirical Mode Decomposition (EMD) was proposed as a new tool for analysis of non-stationary signal. EMD is a time series analysis method that extracts a custom set of bases that reflects the characteristic response of a system. The Intrinsic Mode Functions (IMFs) within the original data can be obtained through EMD. We expect that the change in the amplitude of the special IMF’s envelope spectrum will become larger when fault impulses are present. Based on this idea, we propose a new fault detection method that combines EMD with Hilbert transform. The proposed method is compared with both the Hilbert-Huang transform and the wavelet transform using simulated signal and real signal collected from a gearbox. The results obtained show that the proposed method is effective in capturing the hidden fault impulses.
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Qiang, Li, Chen Xin, Xiao Dengyi, et al. "Subtle Fault Prediction Technique Based on the Integration of Deep Learning and Seismic Spectral Decomposition." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211631-ms.
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Abstract Faults often control the movement and aggregation of oil and gas. With the development of oil fields, the role of subtle faults is becoming more and more important. The accuracy of fault interpretation directly affects the direction of exploration and development. However, due to the limitation of the seismic resolution, it is hard to identify these faults according to routine methods such as coherence, variance, curvature, etc. To overcome such kind of challenge and better match the demand for fine fault identification, a method integrated deep learning and spectral decomposition was proposed.
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Karakatic, Saso, Dusan Fister, Omer Faruk Beyca, and Iztok Fister. "Optimized Class Decomposition for Fault Detection." In 2021 IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2021. http://dx.doi.org/10.1109/cinti53070.2021.9668488.
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Miao, Qiang, Dong Wang, Hong-Zhong Huang, Bin Zheng, and Xianfeng Fan. "Gearbox On-Line Condition Monitoring Using Empirical Mode Decomposition." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86324.
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As a flexible maintenance strategy, Condition Based Maintenance (CBM) has been accepted by industry due to its efficiency and robustness in many engineering practices. Successful implementation of CBM relies on observation of actual health condition of machinery. Therefore, it is crucial to perform condition monitoring in CBM. This paper focuses on quantifying health condition of machinery. Empirical Mode Decomposition (EMD) is employed to decompose signal and extract dominant signatures, which could reflect health condition variation of machinery. Then, a novel index called Health Index (HI) is proposed to describe condition development trends. In order to detect occurrence of early faults, a dynamic threshold is also proposed. In case occurrence of early fault, HI should be higher than its corresponding threshold. This novel condition monitoring method is more appropriate for on-line health monitoring and detection of incipient fault. Two sets of data collected from gearboxes are used to validate the proposed method. The analysis results show that the proposed method is effective in condition monitoring, especially the detection of early faults.
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Papakonstantinou, Nikolaos, Scott Proper, Bryan O’Halloran, and Irem Y. Tumer. "A Plant-Wide and Function-Specific Hierarchical Functional Fault Detection and Identification (HFFDI) System for Multiple Fault Scenarios on Complex Systems." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46447.
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The development of Fault Detection and Identification (FDI) systems for complex mechatronic systems is a challenging process. Many quantitative and qualitative fault detection methods have been proposed in past literature. Few methods address multiple faults, instead an emphasis is placed on accurately proving a single fault exists. The omission of multiple faults regulates the capability of most fault detection methods. The Functional Failure Identification and Propagation (FFIP) framework has been utilized in past research for various applications related to fault propagation in complex systems. In this paper a Hierarchical Functional Fault Detection and Identification (HFFDI) system is proposed. The development of the HFFDI system is based on machine learning techniques, commonly used as a basis for FDI systems, and the functional system decomposition of the FFIP framework. The HFFDI is composed of a plant-wide FDI system and function-specific FDI systems. The HFFDI aims at fault identification in multiple fault scenarios using single fault data sets, when faults happen in different system functions. The methodology is applied to a case study of a generic nuclear power plant with 17 system functions. Compared with a plant-wide FDI system, in multiple fault scenarios the HFFDI gave better results for identifying one fault and also was able to identify more than one faults. The case study results show that in two fault scenarios the HFFDI was able to identify one of the faults with 79% accuracy and both faults with 13% accuracy. In three fault scenarios the HFFDI was able to identify one of the faults with 69% accuracy, two faults with 22% accuracy and all three faults with 1% accuracy.
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Wang, Dong, Qiang Miao, Rui Sun, and Hong-Zhong Huang. "Bearing Fault Diagnosis Using Singular Value Decomposition and Hidden Markov Modeling." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86471.
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Condition monitoring and fault diagnosis of bearings are of practical significance in industry. In order to get a feature containing different fault signatures, this paper uses Wavelet Transform (WT), Wavelet Lifting Scheme (WLS) and Empirical Mode Decomposition (EMD), respectively, to decompose signal into different frequency bands. Then, Singular Value Decomposition (SVD) is utilized to extract intrinsic characteristic of signal from obtained matrix. These singular value vectors are regarded as inputs to Hidden Markov Models (HMM) for identification of machinery health condition. In this research, the fault diagnosis system is validated by motor bearing data, including normal bearings, inner race fault bearings, outer race fault bearings and roller fault bearings. Analysis results show that this method is effective in bearing fault diagnosis and its classification rate is excellent.
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Feng, Zhipeng, Rujiang Hao, Jin Zhang, and Fulei Chu. "Gearbox fault diagnosis based on frame decomposition." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5646219.
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Mohanty, Karunesh Kumar Gupta, and Kota Solomon Raju. "Bearing fault analysis using variational mode decomposition." In 2014 9th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2014. http://dx.doi.org/10.1109/iciinfs.2014.7036617.
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Zhao, Lei, Zude Zhou, Yang Yin, Rong Chen, Quan Liu, and Qin Wei. "Feature Extraction of Rolling Bearing Fault Based on Ensemble Empirical Mode Decomposition and Correlation Dimension." In ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/msec2014-4070.
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Rolling bearing is the core element of a machine, especially used in rotary machine. Its working status and healthy condition directly affect the efficiency and life cycle of a machine. So it is very important to monitor and diagnose the faults of rolling bearings. In this paper, a novel method based on ensemble empirical mode decomposition (EEMD) and improved correlation dimension (CD) is presented to extract fault feature of rolling bearing fault. The conventional CD has two defects, one is sensitive to the noise, and another is difficult to calculate the slope over the linear region (scaling region). In order to reduce the effects of noise, EEMD is used to decompose the components with truly physical meaning from signals. And in order to identify the scaling region and calculate the slope, an improved CD algorithm is proposed to acquire the scaling area automatically and verified by the well-known analytic models such as Lorenz attractor. Finally, the method is applied to detect the fault features of rolling bearings based on vibration signals and the experimental results indicate its applicability and effectiveness in fault diagnosis of the rolling bearings.
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Tsao, Wen-Chang, and Min-Chun Pan. "Multi-Fault Diagnosis of Ball Bearings Using Appropriate IMFs for Envelope Analysis." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48138.
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The traditional envelope analysis is the presence of an effective method for the rolling bearing fault detection. However, all of the resonant frequency bands must be examined on the process of bearing fault detection. To ameliorate the deficiency, this paper presents a novel concept based on the empirical mode decomposition (EMD) and the envelope analysis to choose an appropriate the resonant frequency bands. By virtue of the band-pass property of EMD, the resonant frequency bands will be allocated in the intrinsic mode function (IMF). Moreover, the rolling elements of bearing strike a local fault on the dual faults and the triple faults will be used to validate the capabilities of the proposed method and comparison studies with the traditional envelope analysis will be discussed. The experimental results show that the proposed method can efficiently and correctly diagnose the bearing multi-fault types.
Reports on the topic "Fault decomposition"
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Sargsyan, Khachik, Khachik Sargsyan, Cosmin Safta, et al. Fault Resilient Domain Decomposition Preconditioner for PDEs. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1494624.
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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0616.
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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels. The clustering algorithm can complete the classification without labeled data, but it is limited by the low accuracy. In this paper, the optimized VMD is used to decompose and standardize the vibration signal. Then the correlation-based feature selection method is implemented to obtain the feature results after dimensionality reduction. Finally, the results are input into the classifier combined by K-means and genetic algorithm (GA). By introducing and optimizing the genetic algorithm, the number of classes can be selected automatically, and the accuracy is significantly improved. This method can carry out adaptive multiple fault detection of a diesel engine without labeled data. Compared with many supervised learning algorithms, the proposed method also has high accuracy.