Relevant bibliographies by topics / Automatic fault analysis

Academic literature on the topic 'Automatic fault analysis'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Automatic fault analysis"

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Baek, Sujeong. "System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems." International Journal of Advanced Manufacturing Technology 113, no. 3-4 (January 29, 2021): 955–66. http://dx.doi.org/10.1007/s00170-021-06652-z.

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AbstractAs automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.
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Wu, Jiang, Zhuo Zhang, Jianjun Xu, Jiayu He, Xiaoguang Mao, Xiankai Meng, and Panpan Li. "Detraque: Dynamic execution tracing techniques for automatic fault localization of hardware design code." PLOS ONE 17, no. 9 (September 16, 2022): e0274515. http://dx.doi.org/10.1371/journal.pone.0274515.

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In an error-prone development process, the ability to localize faults is a crucial one. Generally speaking, detecting and repairing errant behavior at an early stage of the development cycle considerably reduces costs and development time. The debugging of the Verilog program takes much time to read the waveform and capture the signal, and in many cases, problem-solving relies heavily on experienced developers. Most existing Verilog fault localization methods utilize the static analysis method to find faults. However, using static analysis methods exclusively may result in some types of faults being inevitably ignored. The use of dynamic analysis could help resolve this issue. Accordingly, in this work, we propose a new fault localization approach for Verilog, named Detraque. After obtaining dynamic execution through test cases, Detraque traces these executions to localize faults; subsequently, it can determine the likelihood of any Verilog statement being faulty and sort the statements in descending order by suspicion score. Through conducting empirical research on real Verilog programs with 61 faulty versions, Detraque can achieve an EXAM score of 18.3%. Thus, Detraque is verified as able to improve Verilog fault localization effectiveness when used as a supplement to static analysis methods.
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Wang, Hong Li, Bing Xu, Xue Dong Xue, and Kan Cheng. "Application of Time-Frequency Analysis & Blind Source Separation to Diagnosis of Faults with Generator Rotor System." Applied Mechanics and Materials 556-562 (May 2014): 2748–51. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2748.

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One method for diagnosis of faults with generator rotor is contrived by combining local wave method and blind source separation. Time-frequency image varies with local wave of different fault signals, and this feature is applied to identify different faults. In order to realize automatic classification of faults, blind source separation is employed for separation of independent components in time-frequency image of local wave of different fault signals, so as to derive projection coefficients for a set of source images. On the basis of this, automatic classification of faults is realized with probability nerve network. Taking fault signal of rotor as an example, this method is investigated, and the validity is proved by experimental results.
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Gloaguen, R., P. R. Marpu, and I. Niemeyer. "Automatic extraction of faults and fractal analysis from remote sensing data." Nonlinear Processes in Geophysics 14, no. 2 (March 22, 2007): 131–38. http://dx.doi.org/10.5194/npg-14-131-2007.

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Abstract. Object-based classification is a promising technique for image classification. Unlike pixel-based methods, which only use the measured radiometric values, the object-based techniques can also use shape and context information of scene textures. These extra degrees of freedom provided by the objects allow the automatic identification of geological structures. In this article, we present an evaluation of object-based classification in the context of extraction of geological faults. Digital elevation models and radar data of an area near Lake Magadi (Kenya) have been processed. We then determine the statistics of the fault populations. The fractal dimensions of fault dimensions are similar to fractal dimensions directly measured on remote sensing images of the study area using power spectra (PSD) and variograms. These methods allow unbiased statistics of faults and help us to understand the evolution of the fault systems in extensional domains. Furthermore, the direct analysis of image texture is a good indicator of the fault statistics and allows us to classify the intensity and type of deformation. We propose that extensional fault networks can be modeled by iterative function system (IFS).
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Ran, Sheng Yi, and Yu Shu Xiong. "Research on Elimination of Electric Power System Fault Based on Electrical Engineering Automatic Control Technology." Advanced Materials Research 898 (February 2014): 771–74. http://dx.doi.org/10.4028/www.scientific.net/amr.898.771.

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In this paper we introduce the computer software fault analysis system to the power fault detection system, and design power fault elimination system of electrical engineering automatic control, and do simulation and experimental study on the performance. When using the turbine blade of electric machinery to detect fault, we can get the automated troubleshooting displacement curve, and using computer simulation to get the electric mechanical stress distribution nephogram. To further verify the effectiveness of the algorithm, we test the frequencies for eight different units, and obtain eight different sets of five order fault diagnosis frequency, and draw the frequency spectrum distribution of frequency response. It provides the theory reference for the automation of power system fault exclusion.
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Parzinger, Michael, Ulrich Wellisch, Lucia Hanfstaengl, Ferdinand Sigg, Markus Wirnsberger, and Uli Spindler. "Identifying faults in the building system based on model prediction and residuum analysis." E3S Web of Conferences 172 (2020): 22001. http://dx.doi.org/10.1051/e3sconf/202017222001.

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The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.
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Xu, Lei, Tiantian Wang, Jingsong Xie, Jinsong Yang, and Guangjun Gao. "A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes." Sensors 22, no. 23 (November 25, 2022): 9150. http://dx.doi.org/10.3390/s22239150.

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Convenient and fast fault diagnosis is the key to improving the service safety and maintenance efficiency of gearboxes. However, the environment and working conditions under complex service conditions are variable, and there is a lack of fault samples in engineering applications. These factors lead to difficulties in intelligent diagnosis methods based on machine learning, while traditional mechanism-based fault diagnosis requires high expertise and long time periods for the manual analysis of data. For the requirements of diagnostic convenience, an automatic fault diagnosis method for gearboxes is proposed in this paper. The method achieves accurate acquisition of rotational speed by constructing a rotational frequency search algorithm. The self-referencing characteristic frequency identification method is proposed to avoid manual signal analysis. On this basis, a framework of anti-interference automatic diagnosis is constructed to realize automatic diagnosis of gear faults. Finally, a gear fault experiment is carried out based on a high-fidelity experimental bench of bogie to verify the effectiveness of the proposed method. The proposed automatic diagnosis method does not rely on a large number of fault samples and avoids the need for diagnosis through professional knowledge, thus saving time for data analysis and promoting the application of fault diagnosis methods.
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Lu, Zhi Li, Shi Guang Hu, Tai Yong Wang, Dong Xiang Chen, and Qing Jian Liu. "Remote Monitoring and Intelligent Fault Diagnosis Technology Research Based on Open CNC System." Advanced Materials Research 819 (September 2013): 234–37. http://dx.doi.org/10.4028/www.scientific.net/amr.819.234.

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In modem manufacturing,the various and complex requirement of industry makes the CNC machine tools more and more automatic and networking.While a remote monitoring and intelligent fault diagnosis system is the basic and indispensable unit for automatic and networking machine tools.This paper is focused on open CNC system,the condition monitoring, and fault diagnosis technology are researched of open CNC system. Integration achieved the CNCmachine tools' status remote monitoring and intelligent fault diagnosis, and detailed analysis of the key technologies for the components of the system. Through effectively integration of the computer technology, Fault Tree Analysis method, or other technologies to enhance the automation, networking and intelligent level of the open CNC system. Keywords: open CNC system; remote monitoring; intelligent fault diagnosis ;Fault Tree Analysis Method;
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Yang, Yiping, Hongyan Wu, and Jianmin Ma. "Electrical System Design and Fault Analysis of Machine Tool Based on Automatic Control." International Journal of Automation Technology 15, no. 4 (July 5, 2021): 547–52. http://dx.doi.org/10.20965/ijat.2021.p0547.

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Automatically controlled machine tools have been used extensively in the industrial field, and fault analysis methods have garnered increasing attention. This paper first describes the software and hardware design of a machine tool and then presents a fault analysis of the machine tool. The fault types of machine tools are analyzed. A signal is obtained from a vibration sensor, the characteristic value is extracted, and the fault is analyzed using a back-propagation neural network (BPNN). The experimental results show that the BPNN yields the best performance when the structure is 8-9-8, and its recognition rate is 97.22% for different types of faults. Meanwhile, the recognition rate of naive Bayes is only 76.73%, and that of a support vector machine is only 85.55%, which is significantly lower than that of the BPNN. The results show that the BPNN is effective in fault analysis and can be promoted and applied more extensively.
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Jia, Zhi Cheng, Zhao Jun Yang, and Gui Xiang Shen. "Early Failure Mode Effect and Criticality Analysis for CNC Machining Tools." Applied Mechanics and Materials 303-306 (February 2013): 1653–56. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1653.

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According to the plentiful early failure data collected on the spots of 30 CNC machining tools with ATC (Automatic Tool Changer), FMECA method is used to analyze the main fault mode and facts which usually make a great possible impact on the reliability of these products during early failure period.We find out the fault positions and the ratios of the fault modes and fault causes. We further analyze the fault modes and fault causes which frequently arise in the subsystems and parts. Through the analyses of FMECA, we learn the weak links of CNC machining tools and provide bases for removing the products faults. Suggestions how to enhance reliability level of these products are proposed.
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Dissertations / Theses on the topic "Automatic fault analysis"

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Nilsson, Markus. "A tool for automatic formal analysis of fault tolerance." Thesis, Linköping University, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4435.

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The use of computer-based systems is rapidly increasing and such systems can now be found in a wide range of applications, including safety-critical applications such as cars and aircrafts. To make the development of such systems more efficient, there is a need for tools for automatic safety analysis, such as analysis of fault tolerance.

In this thesis, a tool for automatic formal analysis of fault tolerance was developed. The tool is built on top of the existing development environment for the synchronous language Esterel, and provides an output that can be visualised in the Item toolkit for fault tree analysis (FTA). The development of the tool demonstrates how fault tolerance analysis based on formal verification can be automated. The generated output from the fault tolerance analysis can be represented as a fault tree that is familiar to engineers from the traditional FTA analysis. The work also demonstrates that interesting attributes of the relationship between a critical fault combination and the input signals can be generated automatically.

Two case studies were used to test and demonstrate the functionality of the developed tool. A fault tolerance analysis was performed on a hydraulic leakage detection system, which is a real industrial system, but also on a synthetic system, which was modeled for this purpose.

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Deosthale, Eeshan Vijay. "Model-Based Fault Diagnosis of Automatic Transmissions." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1542631227815892.

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Abdul-Raheem, Khalid Fatihi. "Automatic bearing fault diagnostics using wavelet analysis and an artificial neural network." Thesis, Glasgow Caledonian University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493933.

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Machinery failure diagnosis is an important component of the Condition Based Maintenance (CBM) activities for most engineering systems. Rolling element bearings are the most common cause of rotating machinery failure. The existence of the amplitude modulation and noises in the faulty bearing vibration signal present challenges to effective fault detection method. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. A new technique for an automated detection and diagnosis of rolling bearing conditions is presented in this thesis. The time-domain vibration signals of rolling bearings with different fault condition are pre-processed using Impulse and Laplace wavelet transforms for rolling bearing fault detection and feature extraction, respectively. The wavelet denoising and the wavelet envelope power spectrums are used for bearing fault detection and diagnosis. Furthermore, the extracted features for the wavelet transform coefficients in time and frequency domain are applied as input vectors to Artificial Neural Networks (ANN) for rolling bearing fault classification. The Impulse and Laplace Wavelets shape and the ANN classifier parameters are optimized using a genetic algorithm (GA). To reduce the computation cost, decrease the size, and enhance the reliability of the ANN, only the predominant wavelet transform scales are selected for feature extraction. The results for both real and simulated bearing vibration data show the effectiveness of the proposed technique for bearing condition identification and classification with very high success rate using minimum input features.
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Valle-Cervantes, Sergio. "Plant-wide monitoring of processes under closed-loop control." Access restricted to users with UT Austin EID Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3035991.

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Labuschagne, P. J. "Automatic clustering with application to time dependent fault detection in chemical processes." Pretoria : [s.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-07062009-142237.

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Ji, Liang. "Improving fault location through interpole analysis of circuit breaker and automatic reclose scheme operation." Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=19009.

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This research concentrates on the development and evaluation of novel single ended impedance based fault location methods, which are easy and economical to implement in practice. The conventional single ended impedance based fault location methods normally suffer from negative effects associated with variability and inaccuracies in fault resistance, distance to fault and from the impact of variable remote end short circuit level. A novel concept of a single ended impedance based fault location method using analysis of 'interpole' states, which arise during the operation of the circuit breaker as the individual poles open sequentially, has been developed. The proposed fault location technique has been shown to have a very high theoretical accuracy by eliminating the aforementioned negative effects associated with conventional single ended impedance based methods. The thesis describes how the developed technique operates through comparing simulated voltage and currents during the interpole states with the actual measured voltage and currents, and searches for a match that may be indicative of fault location. When a match is found within a pre-specified tolerance error from analysis of the initial 'during fault' state, the ranges of corresponding possible fault locations, fault resistances and remote end short circuit levels used in the simulation are noted. The ranges of all possible values are subsequently reduced through analysis of the consecutive interpole stages as each pole of the circuit breaker opens sequentially to finally interrupt the flow of current in all three phases. The final, most accurate, fault location is obtained following on from analysis of the final state. Another single ended impedance based fault location method has been developed that extends the analysis to the operation of single/three phase auto-reclose schemes. Similarly with previous method, the second method also uses the analysis of different system states, which are arisen during the auto-reclose operation, and improves on the accuracy of the method that only analyses the single operation of the circuit breaker. The methods are demonstrated using EMTP/ATP simulation models for a variety of different cases and it is shown how high accuracy has been achieved, with improved performance when compared with conventional single ended impedance based method (Takagi method and network impedance method). Additionally, it is a potentially economic solution, as only local end data is required. The thesis concludes with an overview of ongoing and future work that has the intention of moving the work forward towards implementation within commercially available relay hardware.
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Yang, Hongyu. "Automatic Fault Diagnosis of Rolling Element Bearings Using Wavelet Based Pursuit Features." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16062/1/Hongyu_Yang_Thesis.pdf.

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Today's industry uses increasingly complex machines, some with extremely demanding performance criteria. Failed machines can lead to economic loss and safety problems due to unexpected production stoppages. Fault diagnosis in the condition monitoring of these machines is crucial for increasing machinery availability and reliability. Fault diagnosis of machinery is often a difficult and daunting task. To be truly effective, the process needs to be automated to reduce the reliance on manual data interpretation. It is the aim of this research to automate this process using data from machinery vibrations. This thesis focuses on the design, development, and application of an automatic diagnosis procedure for rolling element bearing faults. Rolling element bearings are representative elements in most industrial rotating machinery. Besides, these elements can also be tested economically in the laboratory using relatively simple test rigs. Novel modern signal processing methods were applied to vibration signals collected from rolling element tests to destruction. These included three advanced timefrequency signal processing techniques, best basis Discrete Wavelet Packet Analysis (DWPA), Matching Pursuit (MP), and Basis Pursuit (BP). This research presents the first application of the Basis Pursuit to successfully diagnosing rolling element faults. Meanwhile, Best basis DWPA and Matching Pursuit were also benchmarked with the Basis Pursuit, and further extended using some novel ideas particularly on the extraction of defect related features. The DWPA was researched in two aspects: i) selecting a suitable wavelet, and ii) choosing a best basis. To choose the most appropriate wavelet function and decomposition tree of best basis in bearing fault diagnostics, several different wavelets and decomposition trees for best basis determination were applied and comparisons made. The Matching Pursuit and Basis Pursuit techniques were effected by choosing a powerful wavelet packet dictionary. These algorithms were also studied in their ability to extract precise features as well as their speed in achieving a result. The advantage and disadvantage of these techniques for feature extraction of bearing faults were further evaluated. An additional contribution of this thesis is the automation of fault diagnosis by using Artificial Neural Networks (ANNs). Most of work presented in the current literature has been concerned with the use of a standard pre-processing technique - the spectrum. This research employed additional pre-processing techniques such as the spectrogram and DWPA based Kurtosis, as well as the MP and BP features that were subsequently incorporated into ANN classifiers. Discrete Wavelet Packets and Spectra, were derived to extract features by calculating RMS (root mean square), Crest Factor, Variance, Skewness, Kurtosis, and Matched Filter. Certain spikes in Matching Pursuit analysis and Basis Pursuit analysis were also used as features. These various alternative methods of pre-processing for feature extraction were tested, and evaluated with the criteria of the classification performance of Neural Networks. Numerous experimental tests were conducted to simulate the real world environment. The data were obtained from a variety of bearings with a series of fault severities. The mechanism of bearing fault development was analysed and further modelled to evaluate the performance of this research methodology. The results of the researched methodology are presented, discussed, and evaluated in the results and discussion chapter of this thesis. The Basis Pursuit technique proved to be effective in diagnostic tasks. The applied Neural Network classifiers were designed as multi layer Feed Forward Neural Networks. Using these Neural Networks, automatic diagnosis methods based on spectrum analysis, DWPA, Matching Pursuit, and Basis Pursuit proved to be effective in diagnosing different conditions such as normal bearings, bearings with inner race and outer race faults, and rolling element faults, with high accuracy. Future research topics are proposed in the final chapter of the thesis to provide perspectives and suggestions for advancing research into fault diagnosis and condition monitoring.
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Yang, Hongyu. "Automatic Fault Diagnosis of Rolling Element Bearings Using Wavelet Based Pursuit Features." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16062/.

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Today's industry uses increasingly complex machines, some with extremely demanding performance criteria. Failed machines can lead to economic loss and safety problems due to unexpected production stoppages. Fault diagnosis in the condition monitoring of these machines is crucial for increasing machinery availability and reliability. Fault diagnosis of machinery is often a difficult and daunting task. To be truly effective, the process needs to be automated to reduce the reliance on manual data interpretation. It is the aim of this research to automate this process using data from machinery vibrations. This thesis focuses on the design, development, and application of an automatic diagnosis procedure for rolling element bearing faults. Rolling element bearings are representative elements in most industrial rotating machinery. Besides, these elements can also be tested economically in the laboratory using relatively simple test rigs. Novel modern signal processing methods were applied to vibration signals collected from rolling element tests to destruction. These included three advanced timefrequency signal processing techniques, best basis Discrete Wavelet Packet Analysis (DWPA), Matching Pursuit (MP), and Basis Pursuit (BP). This research presents the first application of the Basis Pursuit to successfully diagnosing rolling element faults. Meanwhile, Best basis DWPA and Matching Pursuit were also benchmarked with the Basis Pursuit, and further extended using some novel ideas particularly on the extraction of defect related features. The DWPA was researched in two aspects: i) selecting a suitable wavelet, and ii) choosing a best basis. To choose the most appropriate wavelet function and decomposition tree of best basis in bearing fault diagnostics, several different wavelets and decomposition trees for best basis determination were applied and comparisons made. The Matching Pursuit and Basis Pursuit techniques were effected by choosing a powerful wavelet packet dictionary. These algorithms were also studied in their ability to extract precise features as well as their speed in achieving a result. The advantage and disadvantage of these techniques for feature extraction of bearing faults were further evaluated. An additional contribution of this thesis is the automation of fault diagnosis by using Artificial Neural Networks (ANNs). Most of work presented in the current literature has been concerned with the use of a standard pre-processing technique - the spectrum. This research employed additional pre-processing techniques such as the spectrogram and DWPA based Kurtosis, as well as the MP and BP features that were subsequently incorporated into ANN classifiers. Discrete Wavelet Packets and Spectra, were derived to extract features by calculating RMS (root mean square), Crest Factor, Variance, Skewness, Kurtosis, and Matched Filter. Certain spikes in Matching Pursuit analysis and Basis Pursuit analysis were also used as features. These various alternative methods of pre-processing for feature extraction were tested, and evaluated with the criteria of the classification performance of Neural Networks. Numerous experimental tests were conducted to simulate the real world environment. The data were obtained from a variety of bearings with a series of fault severities. The mechanism of bearing fault development was analysed and further modelled to evaluate the performance of this research methodology. The results of the researched methodology are presented, discussed, and evaluated in the results and discussion chapter of this thesis. The Basis Pursuit technique proved to be effective in diagnostic tasks. The applied Neural Network classifiers were designed as multi layer Feed Forward Neural Networks. Using these Neural Networks, automatic diagnosis methods based on spectrum analysis, DWPA, Matching Pursuit, and Basis Pursuit proved to be effective in diagnosing different conditions such as normal bearings, bearings with inner race and outer race faults, and rolling element faults, with high accuracy. Future research topics are proposed in the final chapter of the thesis to provide perspectives and suggestions for advancing research into fault diagnosis and condition monitoring.
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Lennartsson, Richard. "Automatic diagnostic system for I-shift transmission using vibration analysis." Thesis, Linköping University, Automatic Control, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57732.

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This master’s thesis work was performed at Volvo Powertrain in Köping, Sweden, which manufactures gearboxes and integrated transmission systems for heavy vehicles. The thesis is a continuation of a previous master’s thesis performed at the Köping factory in 2009. After manufacturing and assembly, each gearbox is manually validated to ensure the gearbox quality and functionality. When validating the gearbox gears, the operator shifts the gearbox in a predefined manner and listens for irregularities. If an error sound is heard the operator must then locate the source of error. With numerous of cog wheels rotating at the same time this task requires extensive knowledge and experience of the operator. The main objective is to develop an automatic diagnostic system for detection of cog errors and assist the operator in the process of locating the faulty component.

The work consists of two parts. In the first part the automatic diagnostic system is developed and a database of gearbox recordings is stored. The amounts of logged non-faulty gearboxes are significantly much larger (50) than the logged faulty gearboxes (1). Therefore, when determining thresholds needed for the diagnosis, the data obtained from the non-faulty gearboxes are used. Two statistical methods are presented to extract the thresholds. The first method uses an extremevalue distribution and the other method a Gaussian distribution. When validated, both methods did successfully detect on cog faults. In the second part an investigation is made of how shaft imbalance can be detected and implemented in the developed system.

Volvo Powertrain continually follows-up all faults found at the validation station to ensure the quality of their work and eliminate the sources of error. During system testing one logged gearbox was found faulty. The automatic diagnostic system did successfully detect and locate the faulty component which later also was confirmed when the gearbox was dismounted. With only one detected error it is difficult to conclude the system performance and further testing is required. However, during the testing no false detections were made.

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Zornoza, Moreno Enrique. "Model-based approach for automatic generation of IEC-61025 standard compliant fault trees." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-40912.

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Reliability and safety of complex software-intensive systems are proved to be a crucial matter since most of these systems fulfil tasks, where a failure could lead to catastrophic consequences. For example, in space systems such as satellites, a failure could result in the loss of the satellite. Therefore, a certain level of reliability and safety must be assured for such systems to trust the services they provide. Standards set this level and put requirements for the analysis and assurance of these properties using documented evidence. In particular, European Cooperation for Space Standardization (ECSS) standards for space systems require Fault Tree Analysis(FTA) for identifying the causes of system failure and consequently safety hazards, as well as fault trees as evidence for the assurance of reliability and safety. In this thesis, we present a tool supported model-based approach to generate fault tree automatically from an existing system modelling and analysis toolset. CHESS is a system and dependability modelling toolset and integrates Concerto-FLA to enable the support of failure logic analysis. We proposed a model-based transformation from Concerto-FLA to fault tree model and implemented it as an Eclipse plugin in CHESS toolset. A case study is performed in the aerospace domain; more specifically we modelled Attitude Control System (ACS) and automatically generated IEC-61025-compliant fault trees.
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Books on the topic "Automatic fault analysis"

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NASA Formal Methods Workshop (4th 1997 Hampton, Va.). Fourth NASA Langley Formal Methods Workshop. [Hampton, Va: National Aeronautics and Space Administration, Langley Research Center], 1997.

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NASA Formal Methods Workshop (5th 2000 Williamsburg, Va.). Lfm2000: Fifth NASA Langley Formal Methods Workshop : proceedings of a workshop sponsored by the National Aeronautics and Space Administration and held at the Radisson Fort Magruder Hotel & Conference Center, Williamsburg Virginia, June 13-15, 2000. [Hampton, Va: National Aeronautics and Space Administration, Langley Research Center], 2000.

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Symposium on System Modelling, Fault Diagnosis, and Fuzzy Logic and Control (1997 Budapest and Miskolc, Hungary). Proceedings of the Symposium on System Modelling, Fault Diagnosis, and Fuzzy Logic and Control: Budapest and Miskolc, Hungary, May 6-10, 1997. Budapest: Technical University of Budapest, 1997.

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1992), NASA Formal Methods Workshop (2nd. Second NASA Formal Workshop 1992: Proceedings of a workshop. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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Fickelscherer, Richard J., and Daniel L. Chester. Optimal Automated Process Fault Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118481950.

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Breier, Jakub, Xiaolu Hou, and Shivam Bhasin, eds. Automated Methods in Cryptographic Fault Analysis. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11333-9.

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Bavuso, Salvatore J. HiRel: Hybrid Automated Reliability Predictor (HARP) Integrated Reliability Tool System (version 7.0). Hampton, Va: Langley Research Center, 1994.

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Brat, Guillaume. NASA Formal Methods: 5th International Symposium, NFM 2013, Moffett Field, CA, USA, May 14-16, 2013. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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FORMATS 2004 (2004 Grenoble, France). Formal techniques, modelling and analysis of timed and fault-tolerant systems: Joint international conferences on Formal Modelling and Analysis of Timed Systems, FORMATS 2004 and Formal Techniques in Real-Time and Fault-Tolerant Systems, FTRTFT 2004, Grenoble, France, September 22-24, 2004 : proceedings. Berlin: Springer, 2004.

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Y, Lakhnech, Yovine Sergio, LINK (Online service), and FTRTFT 2004 (2004 : Grenoble, France), eds. Formal techniques, modelling and analysis of timed and fault-tolerant systems: Joint international conferences on formal modeling and analysis of timed systems, FORMATS 2004, and formal techniques in real-time and fault -tolerant systems, FTRTFT 2004, Grenoble, France, September 22-24, 2004 : proceedings. Berlin: Springer, 2004.

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Book chapters on the topic "Automatic fault analysis"

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Polian, Ilia, Mael Gay, Tobias Paxian, Matthias Sauer, and Bernd Becker. "Automatic Construction of Fault Attacks on Cryptographic Hardware Implementations." In Automated Methods in Cryptographic Fault Analysis, 151–70. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11333-9_6.

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Shi, Lei, Yuepeng Wang, Rajeev Alur, and Boon Thau Loo. "Automatic Repair for Network Programs." In Tools and Algorithms for the Construction and Analysis of Systems, 353–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99527-0_19.

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AbstractDebugging imperative network programs is a difficult task for operators as it requires understanding various network modules and complicated data structures. For this purpose, this paper presents an automated technique for repairing network programs with respect to unit tests. Given as input a faulty network program and a set of unit tests, our approach localizes the fault through symbolic reasoning, and synthesizes a patch ensuring that the repaired program passes all unit tests. It applies domain-specific abstraction to simplify network data structures and exploits function summary reuse for modular symbolic analysis. We have implemented the proposed techniques in a tool called NetRep and evaluated it on 10 benchmarks adapted from real-world software-defined network controllers. The evaluation results demonstrate the effectiveness and efficiency of NetRep for repairing network programs.
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Salazar-D’antonio, Diego, Nohora Meneses-Casas, Manuel G. Forero, and Oswaldo López-Santos. "Automatic Fault Detection in a Cascaded Transformer Multilevel Inverter Using Pattern Recognition Techniques." In Pattern Recognition and Image Analysis, 378–85. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31332-6_33.

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Wang, Zuowei, Hong Zhang, Dongchao Liu, Shiping E., Kanjun Zhang, Haitao Li, Hengxuan Li, and Zhigang Chen. "New Principle of Fault Data Synchronization for Intelligent Protection Based on Wavelet Analysis." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 850–61. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_87.

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AbstractIn order to eliminate the influence of the delay error of the sampled value in the data link on the longitudinal differential protection device, this paper proposes a protection data self-healing synchronization algorithm based on wavelet transform to calculate the moment of sudden change. First, calculate the mutation amount of the sampled data at each end in real time. When the mutation amount threshold is exceeded, it is determined that the multi-terminal system has a short-circuit fault. Then, according to the sudden change characteristics of the collected current waveform, the wavelet modulus maximum value is used to extract the fault sudden change time of each end data, based on the fault time at one terminal, the automatic compensation for the time differences between this terminal and others are realized, thus a new sampling sequence is formed. The resynchronized sampling sequences are used to calculate the differential current and braking current after fault to ensure the correct action of the protective device. Through theoretical analysis and simulations, the correctness and effectiveness of the proposed algorithm is verified; in addition, it is shown that this algorithm can improve the reliability of actions by the intelligent protection device, thus realizing protections such as multi-terminal differential, wide-area differential, etc.
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Zhu, Kegang, ZhaoGang Zhang, and Qingfu Zeng. "Malfunction Analysis on Reverse-Flighted Screw Automatic Cable Layer Based on Fault Tree." In Advances in Mechanical and Electronic Engineering, 137–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31516-9_24.

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Yang, Junjun, Wei Xu, Shaofeng Liu, Xiancheng Ren, and Xiaotong Xu. "A Rapid Power Flow Analysis Method After UHVDC Fault Considering the Control Strategy of Automatic Equipment." In Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control, 85–97. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9779-0_7.

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Yu, Na Hyeon, and Sujeong Baek. "Fault Detection in Automatic Manufacturing Processes via 2D Image Analysis Using a Combined CNN–LSTM Model." In IFIP Advances in Information and Communication Technology, 11–18. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16407-1_2.

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Zheng, Wenli, and Jinwen Ma. "Automatic Fault Detection for 2D Seismic Data Based on the Seismic Coherence of Mutative Scale Analysis Window." In Intelligence Science II, 391–400. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01313-4_42.

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Tadejko, Pawel, and Waldemar Rakowski. "Singularities Detection System Design for Automatic Analysis of Biomedical Signals and Machine Condition Monitoring and Fault Diagnostics." In Studies in Computational Intelligence, 101–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27446-6_9.

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Putruele, Luciano, Ramiro Demasi, Pablo F. Castro, and Pedro R. D’Argenio. "MaskD: A Tool for Measuring Masking Fault-Tolerance." In Tools and Algorithms for the Construction and Analysis of Systems, 396–403. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99524-9_22.

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AbstractWe present , an automated tool designed to measure the level of fault-tolerance provided by software components. The tool focuses on measuring masking fault-tolerance, that is, the kind of fault-tolerance that allows systems to mask faults in such a way that they cannot be observed by the users. The tool takes as input a nominal model (which serves as a specification) and its fault-tolerant implementation, described by means of a guarded-command language, and automatically computes the masking distance between them. This value can be understood as the level of fault-tolerance provided by the implementation. The tool is based on a sound and complete framework we have introduced in previous work. We present the ideas behind the tool by means of a simple example and report experiments realized on more complex case studies.
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Conference papers on the topic "Automatic fault analysis"

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Cong, Kai, Li Lei, Zhenkun Yang, and Fei Xie. "Automatic fault injection for driver robustness testing." In ISSTA '15: International Symposium on Software Testing and Analysis. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2771783.2771811.

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Hamann, Rainer, Andreas Uhlig, Yiannis Papadopoulos, Erich Ru¨de, Uwe Gra¨tz, Martin Walker, and Rune Lien. "Semi Automatic Failure Analysis Based on Simulation Models." In ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57256.

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Classical risk assessment and risk management which is gaining importance in many industries is usually based on well defined processes and uses techniques like FTA and FMEA. However, classical risk analysis techniques like FTA and FMEA should ideally be automated, at least to some extent and without loss of effectiveness, to enable fast and cost effective iterations of system modelling and risk analysis that can meet the tight cost and time constraints of most offshore projects. This paper is focused on the presentation of a new concept and tool extension for model-based synthesis of fault trees and FMEAs in which these failure analyses are automatically constructed from engineering design models, e.g. simulation models that have been augmented with information about the local propagation of failures. The simulation model is developed in the commercial system modelling tool SimulationX. The proposed process enables the automatic generation of both fault trees and FMEA tables in a single run of the tool, allowing the FMEA and fault trees to share failure data and allowing the FMEA to include failures caused by multiple basic events. As it is a largely automated process, it could be easily iterated to enable the continuous assessment of evolving designs. It provides an automatic generation of fault trees and FMEA tables for multiple top events in a single run of the tool. The potential benefits from application of this technique and tool are substantial and include simplifying the analysis, easing the examination of effects of design modifications on safety and keeping the safety analyses consistent with the design. Furthermore, the presented approach combines the benefits of simulation and risk analysis in one tool. The benefits of this approach are demonstrated by the example of a blow out preventer for a subsea installation valve.
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Henderson, Tim A. D., Andy Podgurski, and Yigit Kucuk. "Evaluating Automatic Fault Localization Using Markov Processes." In 2019 IEEE 19th International Working Conference on Source Code Analysis and Manipulation (SCAM). IEEE, 2019. http://dx.doi.org/10.1109/scam.2019.00021.

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Shi, Zhanqun, Andrew Higson, Lin Zheng, Fengshou Gu, and Andrew Ball. "Automatic Fault Detection Using a Model-Based Approach in the Frequency Domain." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95103.

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In this paper, the model-based approach is introduced into rotation machinery fault detection to achieve an automatic feature extraction. The paper starts with a brief review of the model-based approach, including model development, residual generation and fault detection and diagnosis. The applicability of this approach to rotation machinery is then considered. In order to overcome difficulties arising from phase shift and random measurements, the statistical performance of the vibration of rotation machinery is analysed in both time and frequency domains. A consistence model is developed using stochastic process theory. After model validation, the model-based approach is implemented in AC motor fault detection. The residual is generated by comparing the new measurement and the model prediction, by both subtraction and division. Fault detection results prove that the model-based approach is applicable to fault feature extraction for rotation machinery in the frequency domain.
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Carrillat, A., H. G. Borgos, T. Randen, L. Sonneland, L. Kvamme, and K. Hansch. "Fault Systems Analysis Using Automatic Fault Displacement Estimates – A Case Study." In 66th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2004. http://dx.doi.org/10.3997/2214-4609-pdb.3.b037.

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Wada, S. I., and T. Nakamura. "Automatic Fault Tracing Using an E-Beam Tester with Reference to a Good Sample." In ISTFA 1997. ASM International, 1997. http://dx.doi.org/10.31399/asm.cp.istfa1997p0243.

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Abstract Locating fault origins of defective logic LSls requires expensive equipment, such as electron beam testers and LSI testers. In order to maximize the utilization of such equipment in achieving high fault analysis throughput as well as to save manpower, the authors are developing an automatic fault tracing system which locates the fault origin overnight without human assistance through control of an electron beam tester and an LSI tester. The system traces backwards via the fault propagation path and locates the fault origin by comparing the behavior of a faulty LSI sample with that of a good LSI sample. Sample exchange in a vacuum chamber is achieved through a dual chip loading mechanism. After initial setting, fault location is accomplished without human assistance by fully automated operations, such as fine tuning SEM images of LSI surfaces, aligning points by robust pattern matching between SEM images and layout data, acquiring voltage contrast images with high contrasts and judging logical voltage levels from the images. A prototype version of this system successfully backtraced to the fault origin of an LSI with 20 k gates in 8 hours.
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Xiang, Jianwen, and Kazuo Yanoo. "Automatic Static Fault Tree Analysis from System Models." In 2010 IEEE 16th Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE, 2010. http://dx.doi.org/10.1109/prdc.2010.35.

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Mendel, E., L. Z. Mariano, I. Drago, S. Loureiro, T. W. Rauber, F. M. Varejao, and R. J. Batista. "Automatic bearing fault pattern recognition using vibration signal analysis." In 2008 IEEE International Symposium on Industrial Electronics (ISIE 2008). IEEE, 2008. http://dx.doi.org/10.1109/isie.2008.4677026.

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Balbin, Jessie R., Febus Reidj G. Cruz, Mary Anne E. Latina, Jon Ervin A. Abu, Carlo G. Sino, Paolo E. Ubaldo, and Christelle Jianne T. Zulueta. "Fault analysis for automatic transmission vehicle using autoregressive modelling." In 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). IEEE, 2017. http://dx.doi.org/10.1109/hnicem.2017.8269451.

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Yin, Yujie, Saman Alaeddini, and Yong Fu. "Automatic Fault Analysis and Visualization of Digital Substation Event." In 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020. http://dx.doi.org/10.1109/pesgm41954.2020.9281882.

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Reports on the topic "Automatic fault analysis"

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Butzbaugh, Joshua, Abraham SD Tidwell, and Chrissi Antonopoulos. Automatic Fault Detection & Diagnostics: Residential Market Analysis. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1670423.

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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.
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Shoukas, Greg, Marcus Bianchi, and Michael Deru. Analysis of Fault Data Collected from Automated Fault Detection and Diagnostic Products for Packaged Rooftop Units. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1660228.

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Shoukas, Greg, Marcus Bianchi, and Michael Deru. Analysis of Fault Data Collected from Automated Fault Detection and Diagnostic Products for Packaged Rooftop Units. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1665808.

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