Journal articles: 'Diagnostics and prognostics'

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Thongboonkerd, Visith. "Clinical proteomics: towards diagnostics and prognostics." Blood 109, no. 12 (June 15, 2007): 5075–76. http://dx.doi.org/10.1182/blood-2007-03-081992.

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Coticchia, Christine M., Jiang Yang, and Marsha A. Moses. "Ovarian Cancer Biomarkers: Current Options and Future Promise." Journal of the National Comprehensive Cancer Network 6, no. 8 (September 2008): 795–802. http://dx.doi.org/10.6004/jnccn.2008.0059.

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As more effective, less toxic cancer drugs reach patients, the need for accurate and reliable cancer diagnostics and prognostics has become widely appreciated. Nowhere is this need more dire than in ovarian cancer; here most women are diagnosed late in disease progression. The ability to sensitively and specifically predict the presence of early disease and its status, stage, and associated therapeutic efficacy has the potential to revolutionize ovarian cancer detection and treatment. This article reviews current ovarian cancer diagnostics and prognostics and potential biomarkers that are being studied and validated. Some of the most recent molecular approaches being used to identify genes and proteins are presented, which may represent the next generation of ovarian cancer diagnostics and prognostics.

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Abbasi, Amirhassan, Foad Nazari, and C. Nataraj. "On Modeling of Vibration and Crack Growth in a Rotor for Prognostics." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 9. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1193.

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Prognostics and health management (PHM) include comprehensive engineering approaches that evaluate the real-time health condition of an asset and predict its future states under the actual operating conditions. This predictive ability would result in efficient maintenance approaches such as Condition Based Maintenance (CBM) that can set maintenance strategies optimally and reduce the life cycle costs. Diagnostics and Prognostics are two major concepts in PHM. Detection, Isolation and Identification of faults are done by diagnostics while prognostics deals with estimation of future states. Mechanical fatigue phenomenon that causes crack initiation and propagation is considered as a common reason for failure in mechanical parts. Hence, diagnostics and prognostics of the crack initiation and propagation have been the subject of many research papers recently. The current paper presents a diagnostics and prognostics method capable of detecting the crack initiation and propagation in a rotor under cyclic loading. At the first step, the coupled equations of rotor motion and crack growth are obtained. An extended model of Paris–Erdogan equation is used for crack growth modeling. The coupled equations are solved numerically. A set of features are extracted from the dynamic response of the rotor for a range of crack lengths. A dataset is compiled including features of response, operating frequency, crack length and number of cycles remained until reaching the critical crack length. With the objective of generalization of the results, the dataset is used for creating a model using an Artifical Neural Network (ANN). In the trained ANN the inputs are the operating speed and the outputs are the crack length and the remaining useful life (RUL) that address the diagnostics and prognostics objectives, respectively.

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Petronic, Ivana, Milena Markovic, Dragana Cirovic, Dragana Dzamic, Ana Marsavelski, and Gordana Nikolic. "Paralysis plexus brachialis - diagnostics and prognostics protocol." Srpski arhiv za celokupno lekarstvo 132, suppl. 1 (2004): 58–61. http://dx.doi.org/10.2298/sarh04s1058p.

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Traction injuries of the brachial plexus, if obstetrical, are diagnosed immediately upon birth based on clinical features, while the type and the degree of injury are confirmed by neurophysiological examination. In such cases, physical therapy is promptly applied and followed up until the age of three months, when, after consultation with neurosurgeon, either physical therapy is continued or surgery is performed. In traumatic injuries, based on clinical, neurological and neurophysiological findings, necessary surgical or pre- and postoperative physiatric interventions are performed. Timely diagnostics and therapy of brachial plexus injuries, followed by recovery of paralytic muscle motor function, enable motion coordination and prevention of contractures. From 2000-2004, 181 cases of brachial plexus birth trauma and 26 cases of brachial plexus traumatic lesions were diagnosed and treated in our institution. Among patients, there were 107 boys and 74 girls with birth injury of the brachial plexus, and 16 boys and 8 girls with traction injury of the brachial plexus sustained in traffic accident. Physical treatment involved combined thermo-, electro-, and kinesitherapy, with alignment of extremities. Upon completion of any treatment session and clinical and neurophysiological examinations, doctors? consultation determined whether to continue with physical therapy or to perform surgery followed by physical therapy with rehabilitation until achieving the maximal motor recovery. The analysis of results showed that functional and motor recovery was best if therapy was initiated immediately after the obstetrical injury or following the surgery. Therapeutic approach was individualized and depended on the level and degree of lesions. Thus, maximal motor and functional recovery of the injured extremity was achieved, with work therapy and professional orientation of such patients.

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Reuben, Lim Chi Keong, and David Mba. "Diagnostics and prognostics using switching Kalman filters." Structural Health Monitoring: An International Journal 13, no. 3 (February 24, 2014): 296–306. http://dx.doi.org/10.1177/1475921714522844.

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Ng, Kam W. "Integrated Diagnostics and Prognostics of Rotating Machinery." International Journal of Rotating Machinery 5, no. 1 (1999): 35–40. http://dx.doi.org/10.1155/s1023621x99000032.

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This paper provides an overview of current research efforts in integrated diagnostics and prognostics of rotating machinery. Specifically, the following topics are discussed: sensing techniques and sensors; signal detection, identification and extraction; classification of faults; predictive failure models; data/model fusion; information management; and man–machine interface. Technical issues, recommendations, and future research directions are also addressed.

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Kirwan, Alan, Marta Utratna, Michael E. O’Dwyer, Lokesh Joshi, and Michelle Kilcoyne. "Glycosylation-Based Serum Biomarkers for Cancer Diagnostics and Prognostics." BioMed Research International 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/490531.

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Cancer is the second most common cause of death in developed countries with approximately 14 million newly diagnosed individuals and over 6 million cancer-related deaths in 2012. Many cancers are discovered at a more advanced stage but better survival rates are correlated with earlier detection. Current clinically approved cancer biomarkers are most effective when applied to patients with widespread cancer. Single biomarkers with satisfactory sensitivity and specificity have not been identified for the most common cancers and some biomarkers are ineffective for the detection of early stage cancers. Thus, novel biomarkers with better diagnostic and prognostic performance are required. Aberrant protein glycosylation is well known hallmark of cancer and represents a promising source of potential biomarkers. Glycoproteins enter circulation from tissues or blood cells through active secretion or leakage and patient serum is an attractive option as a source for biomarkers from a clinical and diagnostic perspective. A plethora of technical approaches have been developed to address the challenges of glycosylation structure detection and determination. This review summarises currently utilised glycoprotein biomarkers and novel glycosylation-based biomarkers from the serum glycoproteome under investigation as cancer diagnostics and for monitoring and prognostics and includes details of recent high throughput and other emerging glycoanalytical techniques.

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Baruah, P., and R. B. Chinnam *. "HMMs for diagnostics and prognostics in machining processes." International Journal of Production Research 43, no. 6 (March 15, 2005): 1275–93. http://dx.doi.org/10.1080/00207540412331327727.

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Li Liu, K. P. Logan, D. A. Cartes, and S. K. Srivastava. "Fault Detection, Diagnostics, and Prognostics: Software Agent Solutions." IEEE Transactions on Vehicular Technology 56, no. 4 (July 2007): 1613–22. http://dx.doi.org/10.1109/tvt.2007.897219.

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Sai Sarathi Vasan, Arvind, Bing Long, and Michael Pecht. "Diagnostics and Prognostics Method for Analog Electronic Circuits." IEEE Transactions on Industrial Electronics 60, no. 11 (November 2013): 5277–91. http://dx.doi.org/10.1109/tie.2012.2224074.

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Booyse, Wihan, Daniel N. Wilke, and Stephan Heyns. "Deep digital twins for detection, diagnostics and prognostics." Mechanical Systems and Signal Processing 140 (June 2020): 106612. http://dx.doi.org/10.1016/j.ymssp.2019.106612.

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Kapusta, Stephanie Julia. "Intersex Diagnostics and Prognostics: Imposing Sex-Predicate Determinacy." Topoi 36, no. 3 (October 9, 2015): 539–48. http://dx.doi.org/10.1007/s11245-015-9354-z.

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Tian, Zhigang, and Wilson Wang. "Special Issue on Machine Fault Diagnostics and Prognostics." Chinese Journal of Mechanical Engineering 30, no. 6 (October 23, 2017): 1283–84. http://dx.doi.org/10.1007/s10033-017-0197-y.

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Lopez-Siles, Mireia, Sylvia H. Duncan, L. Jesús Garcia-Gil, and Margarita Martinez-Medina. "Faecalibacterium prausnitzii: from microbiology to diagnostics and prognostics." ISME Journal 11, no. 4 (January 3, 2017): 841–52. http://dx.doi.org/10.1038/ismej.2016.176.

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Thongboonkerd, Visith. "Urinary proteomics: towards biomarker discovery, diagnostics and prognostics." Molecular BioSystems 4, no. 8 (2008): 810. http://dx.doi.org/10.1039/b802534g.

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Trinh, Hung-Cuong, and Yung-Keun Kwon. "A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction." Applied Sciences 10, no. 1 (January 3, 2020): 368. http://dx.doi.org/10.3390/app10010368.

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Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine learning. In general, the best performing algorithm and the optimal hyper-parameters suitable for each subtask are varied across the characteristics of datasets. Therefore, it is challenging to develop a general diagnostic/prognostic framework that can automatically identify the best subtask algorithms and the optimal involved parameters for a given dataset. To resolve this problem, we propose a new framework based on an ensemble of genetic algorithms (GAs) that can be used for both the fault-type classification and RUL prediction. Our GA is combined with a specific machine-learning method and then tries to select the best algorithm and optimize the involved parameter values in each subtask. In addition, our method constructs an ensemble of various prediction models found by the GAs. Our method was compared to a traditional grid-search over three benchmark datasets of the fault-type classification and the RUL prediction problems and showed a significantly better performance than the latter. Taken together, our framework can be an effective approach for the fault-type and RUL prediction of various machinery systems.

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Li, Y., C. Zhang, T. R. Kurfess, S. Danyluk, and S. Y. Liang. "Diagnostics and prognostics of a single surface defect on roller bearings." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 214, no. 9 (September 1, 2000): 1173–85. http://dx.doi.org/10.1243/0954406001523614.

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Bearing condition monitoring has been the focus of a wide range of studies over the past years. Current monitoring techniques that focus on the identification of faults present in a bearing have various limitations. Typically they are applicable only under well-defined, specific and pre-calibrated operating conditions, thereby preventing continuous monitoring of a system operating in a variant environment. They are often limited in damage severityestimation and prognostic capability. This, in turn, prevents the development of optimal maintenance scheduling in favour of overall system safety and productivity. Research presented in this paper has yielded results that have extended bearing diagnostics and prognostics to address these limitations and to achieveoptimal machinery maintenance scheduling. This paper discusses the current research status on the development of a new signal processing method with noise cancellation capability to provide early defect detection, the establishment of a diagnostic model to estimate bearing defect severity under variant conditions and the formulation of an adaptively tuned defect propagation model to track thetime-variant nature of defect growth for the forecasting of bearing remaining utility.

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Hagmeyer, Simon, Peter Zeiler, and Marco F. Huber. "On the Integration of Fundamental Knowledge about Degradation Processes into Data-Driven Diagnostics and Prognostics Using Theory-Guided Data Science." PHM Society European Conference 7, no. 1 (June 29, 2022): 156–65. http://dx.doi.org/10.36001/phme.2022.v7i1.3352.

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In Prognostics and Health Management, there are three main approaches for implementing diagnostic and prognostic applications. These approaches are data-driven methods, physical model-based methods, and combinations of them, in the form of hybrid methods. Each of them has specific advantages but also limitations for their purposeful implementation. In the case of data-driven methods, one of the main limitations is the availability of sufficient training data that adequately cover the relevant state space. For model-based methods, on the other hand, it is often the case that the degradation process of the considered technical system is of significant complexity. In such a scenario physics-based modeling requires great effort or is not possible at all. Combinations of data-driven and model-based approaches in form of hybrid approaches offer the possibility to partially mitigate the shortcomings of the other two approaches, however, require a sufficiently detailed data-driven and physics-based model. This paper addresses the transitional field between data-driven and hybrid approaches. Despite the issues of formulating a physics-based model that provides a representation of the degradation process, basic knowledge of the considered system and of the laws governing its degradation process is usually available. Integration of such knowledge into a machine learning process is part of a research field that is either called theory-guided data science, (physics) informed machine learning, physics-based learning or physics guided machine learning. First, the state of research in Prognostics and Health Management on methods of this field is presented and existing research gaps are outlined. Then, a concept is introduced for incorporating fundamental knowledge, such as monotonicity constraints, into data-driven diagnostic and prognostic applications using approaches from theory-guided data science. A special aspect of this concept is its cross-application usability through the consideration of knowledge that repeatedly occurs in diagnostics and prognostics. This is, for example, knowledge about physically justified boundaries whose compliance makes a prediction of the data-driven model plausible in the first place.

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Goh, Yeh Huann. "Data-driven system prognostics and diagnostics for SMT component placement machines." HKIE Transactions 30, no. 1 (2023): 116–23. http://dx.doi.org/10.33430/v30n1thie-2022-0047.

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Prognostic-based maintenance analyses past events and predicts the future state of a machine based on the understanding of the degradation function of the machine’s components. Diagnostics-based maintenance tests equipment according to a fixed routine for a machine’s proper functioning and reliability. Current Surface-mount Technology (SMT) machines are not equipped with self-prognostic and diagnostic functions. In this paper, a system prognostic and diagnostic method is proposed, implemented in software, for estimating a machine’s health condition and faulty components of a SMT component placement machine outfitted with machine logs that consist of take-up count, miss count and time information. At each execution period the method processes features extracted from the machine logs to obtain a set of parity parameters, which are further used to analyse the machine. The prognostic algorithm computes the health status indicator of the component placement machine. The computed final status indicator is compared to a threshold value to check the system’s health condition. The diagnostic algorithm predicts and identifies the faulty pick-up nozzles and faulty input trays. The proposed algorithms minimise the effects of faulty components on production lines and assist to produce optimal maintenance decisions and reliability functions for equipment.

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Arias Chao, Manuel, Chetan Kulkarni, Kai Goebel, and Olga Fink. "Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics." Data 6, no. 1 (January 13, 2021): 5. http://dx.doi.org/10.3390/data6010005.

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A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health, respectively, fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.

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Ruiz-Tagle Palazuelos, Andrés, Enrique López Droguett, and Rodrigo Pascual. "A novel deep capsule neural network for remaining useful life estimation." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 1 (August 7, 2019): 151–67. http://dx.doi.org/10.1177/1748006x19866546.

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With the availability of cheaper multi-sensor systems, one has access to massive and multi-dimensional sensor data for fault diagnostics and prognostics. However, from a time, engineering and computational perspective, it is often cost prohibitive to manually extract useful features and to label all the data. To address these challenges, deep learning techniques have been used in the recent years. Within these, convolutional neural networks have shown remarkable performance in fault diagnostics and prognostics. However, this model present limitations from a prognostics and health management perspective: to improve its feature extraction generalization capabilities and reduce computation time, ill-based pooling operations are employed, which require sub-sampling of the data, thus loosing potentially valuable information regarding an asset’s degradation process. Capsule neural networks have been recently proposed to address these problems with strong results in computer vision–related classification tasks. This has motivated us to extend capsule neural networks for fault prognostics and, in particular, remaining useful life estimation. The proposed model, architecture and algorithm are tested and compared to other state-of-the art deep learning models on the benchmark Commercial Modular Aero Propulsion System Simulation turbofans data set. The results indicate that the proposed capsule neural networks are a promising approach for remaining useful life prognostics from multi-dimensional sensor data.

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Byington, C. S., M. J. Watson, J. S. Sheldon, and G. M. Swerdon. "Shaft coupling model-based prognostics enhanced by vibration diagnostics." Insight - Non-Destructive Testing and Condition Monitoring 51, no. 8 (August 2009): 420–25. http://dx.doi.org/10.1784/insi.2009.51.8.420.

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Beshears, R., and L. Butler. "Designing for health - A methodology for integrated diagnostics/prognostics." IEEE Instrumentation and Measurement Magazine 9, no. 4 (August 2006): 22–28. http://dx.doi.org/10.1109/mim.2006.1664038.

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Randall, R. B. "Applications of Spectral Kurtosis in Machine Diagnostics and Prognostics." Key Engineering Materials 293-294 (September 2005): 21–32. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.21.

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Many machine faults, such as local defects in bearings and gears, manifest themselves in vibration signals as a series of impulsive events. Kurtosis is a measure of the impulsiveness of a signal, and spectral kurtosis (SK) gives an indication of how the kurtosis (of a bandpass filtered signal) varies with frequency. This not only gives an indication of the frequency bands to be processed, but can also be used to generate a filter to extract the most impulsive part of a signal. The first step in calculating SK is to perform a time/frequency decomposition of the signal, and then calculate the kurtosis for each frequency line. The paper compares the original STFT (short time Fourier transform) with wavelet analysis for the time/frequency decomposition, and for determining the optimum combination of centre frequency and bandwidth for maximizing the SK. The paper also describes how the SK can be enhanced by “prewhitening” the signal using an autoregressive (AR) model, this sometimes revealing an incipient fault at a much earlier stage.

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Yilu Zhang, G. W. Gantt, M. J. Rychlinski, R. M. Edwards, J. J. Correia, and C. E. Wolf. "Connected Vehicle Diagnostics and Prognostics, Concept, and Initial Practice." IEEE Transactions on Reliability 58, no. 2 (June 2009): 286–94. http://dx.doi.org/10.1109/tr.2009.2020484.

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Trego,, A., D. Price,, M. Hedley,, P. Corrigan,, I. Cole,, and T. Muster,. "Development of a System for Corrosion Diagnostics and Prognostics." Corrosion Reviews 25, no. 1-2 (April 2007): 161–78. http://dx.doi.org/10.1515/corrrev.2007.25.1-2.161.

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Soper, Steven A., Kathlynn Brown, Andrew Ellington, Bruno Frazier, Guillermo Garcia-Manero, Vincent Gau, Steven I. Gutman, et al. "Point-of-care biosensor systems for cancer diagnostics/prognostics." Biosensors and Bioelectronics 21, no. 10 (April 2006): 1932–42. http://dx.doi.org/10.1016/j.bios.2006.01.006.

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Jain, Amit Kumar, Priyansha Chouksey, Ajith Kumar Parlikad, and Bhupesh Kumar Lad. "Distributed Diagnostics, Prognostics and Maintenance Planning: Realizing Industry 4.0." IFAC-PapersOnLine 53, no. 3 (2020): 354–59. http://dx.doi.org/10.1016/j.ifacol.2020.11.057.

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Grossman, Iris, Michael W. Lutz, Donna G. Crenshaw, Ann M. Saunders, Daniel K. Burns, and Allen D. Roses. "Alzheimer’s disease: diagnostics, prognostics and the road to prevention." EPMA Journal 1, no. 2 (June 2010): 293–303. http://dx.doi.org/10.1007/s13167-010-0024-3.

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Cruz-Manzo, Samuel, Vili Panov, and Chris Bingham. "Compressor Fouling Diagnostics and Prognostics in a Gas Turbine." International Journal of Gas Turbine, Propulsion and Power Systems 15, no. 1 (2024): 9–14. http://dx.doi.org/10.38036/jgpp.15.1_9.

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Dhauria, Mrinmay, Ritwick Mondal, Shramana Deb, Gourav Shome, Dipanjan Chowdhury, Shramana Sarkar, and Julián Benito-León. "Blood-Based Biomarkers in Alzheimer’s Disease: Advancing Non-Invasive Diagnostics and Prognostics." International Journal of Molecular Sciences 25, no. 20 (October 10, 2024): 10911. http://dx.doi.org/10.3390/ijms252010911.

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Alzheimer’s disease (AD), the most prevalent form of dementia, is expected to rise dramatically in incidence due to the global population aging. Traditional diagnostic approaches, such as cerebrospinal fluid analysis and positron emission tomography, are expensive and invasive, limiting their routine clinical use. Recent advances in blood-based biomarkers, including amyloid-beta, phosphorylated tau, and neurofilament light, offer promising non-invasive alternatives for early AD detection and disease monitoring. This review synthesizes current research on these blood-based biomarkers, highlighting their potential to track AD pathology and enhance diagnostic accuracy. Furthermore, this review uniquely integrates recent findings on protein-protein interaction networks and microRNA pathways, exploring novel combinations of proteomic, genomic, and epigenomic biomarkers that provide new insights into AD’s molecular mechanisms. Additionally, we discuss the integration of these biomarkers with advanced neuroimaging techniques, emphasizing their potential to revolutionize AD diagnostics. Although large-scale validation is still needed, these biomarkers represent a critical advancement toward more accessible, cost-effective, and early diagnostic tools for AD.

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Wani, Javaid Ahmad, Sabhiya Majid, Zuha Imtiyaz, Muneeb U. Rehman, Rana M. Alsaffar, Naveed Nazir Shah, Sultan Alshehri, Mohammed M. Ghoneim, and Syed Sarim Imam. "MiRNAs in Lung Cancer: Diagnostic, Prognostic, and Therapeutic Potential." Diagnostics 12, no. 7 (July 1, 2022): 1610. http://dx.doi.org/10.3390/diagnostics12071610.

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Lung cancer is the dominant emerging factor in cancer-related mortality around the globe. Therapeutic interventions for lung cancer are not up to par, mainly due to reoccurrence/relapse, chemoresistance, and late diagnosis. People are currently interested in miRNAs, which are small double-stranded (20–24 ribonucleotides) structures that regulate molecular targets (tumor suppressors, oncogenes) involved in tumorigeneses such as cell proliferation, apoptosis, metastasis, and angiogenesis via post-transcriptional regulation of mRNA. Many studies suggest the emerging role of miRNAs in lung cancer diagnostics, prognostics, and therapeutics. Therefore, it is necessary to intensely explore the miRNOME expression of lung tumors and the development of anti-cancer strategies. The current review focuses on the therapeutic, diagnostic, and prognostic potential of numerous miRNAs in lung cancer.

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KOUCKY, Miroslav, and David VALIŠ. "SUITABLE APPROACH FOR NON-TRADITIONAL DETERMINATION OF SYSTEM HEALTH AND PROGNOSTICS." Scientific Journal of the Military University of Land Forces 159, no. 1 (January 3, 2011): 123–34. http://dx.doi.org/10.5604/01.3001.0002.2884.

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Nowadays the system requirements are set up and evaluated in various manners. We have plenty of excellent options available taking into account item technical state or/and other states and talking about diagnostic options. Tribodiagnostics is one of the independent and good parts to assess the system condition. The paper deals with the mathematical processing, monitoring and analysis of the oil field data got as a result from the laser spectrography in frame of the tribodiagnostic oil tests. This diagnostic approach has been currently used for oil diagnostic results comparison – usually for comparison of expected system state determined by laser spectrography only. There is not known deeper relation of oil diagnostics data and system related consequences (like determining the terminal state and service optimization – maintenance for instance).

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Kurtenkov, Oleg. "Profiling of Naturally Occurring Antibodies to the Thomsen-Friedenreich Antigen in Health and Cancer: The Diversity and Clinical Potential." BioMed Research International 2020 (March 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/9747040.

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The Thomsen-Friedenreich (TF) antigen is expressed in a majority of human tumors due to aberrant glycosylation in cancer cells. There is strong evidence that humoral immune response to TF represents an effective mechanism for the elimination of cancer cells that express TF-positive glycoconjugates. The presence of naturally occurring antibodies to tumor-associated TF and cancer-specific changes in their levels, isotype distribution and interrelation, avidity, and glycosylation profile make these Abs a convenient and ubiquitous marker for cancer diagnostics and prognostics. In this review, we attempt to summarize the latest data on the potential of TF-specific Abs for cancer diagnostics and prognostics.

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Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Wohlleben, Amelie Bender, and Walter Sextro. "Rule-based Diagnostics of a Production Line." PHM Society European Conference 6, no. 1 (June 29, 2021): 10. http://dx.doi.org/10.36001/phme.2021.v6i1.3042.

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In the industry 4.0 era, there is a growing need to transform unstructured data acquired by a multitude of sources into information and subsequently into knowledge to improve the quality of manufactured products, to boost production, for predictive maintenance, etc. Data-driven approaches, such as machine learning techniques, are typically employed to model the underlying relationship from data. However, an increase in model accuracy with state-of-the-art methods, such as deep convolutional neural networks, results in less interpretability and transparency. Due to the ease of implementation, interpretation and transparency to both domain experts and non-experts, a rule-based method is proposed in this paper, for prognostics and health management (PHM) and specifically for diagnostics. The proposed method utilizes the most relevant sensor signals acquired via feature extraction and selection techniques and expert knowledge. As a case study, the presented method is evaluated on data from a real-world quality control set-up provided by the European prognostics and health management society (PHME) at the conference’s 2021 data challenge. With the proposed method, our team took the third place, capable of successfully diagnosing different fault modes, irrespective of varying conditions.

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Saini, Aman, Yash Pershad, Hassan Albadawi, Malia Kuo, Sadeer Alzubaidi, Sailendra Naidu, M.-Grace Knuttinen, and Rahmi Oklu. "Liquid Biopsy in Gastrointestinal Cancers." Diagnostics 8, no. 4 (October 29, 2018): 75. http://dx.doi.org/10.3390/diagnostics8040075.

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Liquid biopsy is the sampling of any biological fluid in an effort to enrich and analyze a tumor’s genetic material. Peripheral blood remains the most studied liquid biopsy material, with circulating tumor cells (CTC’s) and circulating tumor DNA (ctDNA) allowing the examination and longitudinal monitoring of a tumors genetic landscape. With applications in cancer screening, prognostic stratification, therapy selection and disease surveillance, liquid biopsy represents an exciting new paradigm in the field of cancer diagnostics and offers a less invasive and more comprehensive alternative to conventional tissue biopsy. Here, we examine liquid biopsies in gastrointestinal cancers, specifically colorectal, gastric, and pancreatic cancers, with an emphasis on applications in diagnostics, prognostics and therapeutics.

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Tanwar, Monika, Hyunseok Park, and Nagarajan Raghavan. "Multistate Diagnosis and Prognosis of Lubricating Oil Degradation Using Sticky Hierarchical Dirichlet Process–Hidden Markov Model Framework." Applied Sciences 11, no. 14 (July 18, 2021): 6603. http://dx.doi.org/10.3390/app11146603.

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In this study, we present a state-based diagnostic and prognostic methodology for lubricating oil degradation based on a nonparametric Bayesian approach, i.e., sticky hierarchical Dirichlet process–hidden Markov model (HDP-HMM). An accurate health state-space assessment for diagnostics and prognostics has always been unobservable and hypothetical in the past. The lubrication condition monitoring (LCM) data is generally segregated as “healthy or unhealthy”, representing a binary state-based perspective to the problem. This two-state performance-based formulation poses limitations to the precision and accuracy of the diagnosis and prognosis for real data wherein there may be multiple states of discrete performance that are characteristic of the system functionality. In particular, the reversible and nonlinear time-series trends of degradation data increase the complexity of state-based modeling. We propose a multistate diagnostic and prognostic framework for LCM data in the wear-out phase (i.e., the unhealthy portion of degradation data), accounting for irregular oil replenishment and oil change effects (i.e., nonlinearity in the degradation signal). The LCM data is simulated for an elementary mechanical system with four components. The sticky HDP sets the prior for the HMM parameters. The unsupervised learning over infinite observations and emission reveals four discrete health states and helps estimate the associated state transition probabilities. The inferred state sequence provides information relating to the state dynamics, which provides further guidance to maintenance decision making. The decision making is further backed by prognostics based on the conditional reliability function and mean residual life estimation.

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Rengasamy, Divish, Mina Jafari, Benjamin Rothwell, Xin Chen, and Grazziela P. Figueredo. "Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management." Sensors 20, no. 3 (January 28, 2020): 723. http://dx.doi.org/10.3390/s20030723.

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Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system’s prognostics and diagnostics without modifying the models’ architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions.

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Du, Xinyu, Lichao Mai, and Hossein Sadjadi. "Fault Diagnostics and Prognostics for Vehicle Springs and Stablizer Bar." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 10. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1129.

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Vehicle springs and stabilizer bar are critical suspension components impacting vehicle riding and handling experience. Diagnostics and prognostics of springs and stabilizer bar can improve customer perceived quality, reduce repair cost and increase up-time for fleet vehicles. It’s even more important for autonomous vehicles, since there is no human driver to sense fault symptoms. Currently, there is no production solution to automatically diagnose and prognose spring and stabilizer bar failures, and most research work is suffered by various noise factors. In this work, a novel solution based on static ramp test is proposed to isolate and localize spring and stabilizer bar faults. With limited number of longitudinal and lateral acceleration measurements, the solution can quickly and effectively isolate faulty spring, disconnected stabilizer bar, loose bushing and loose end link. The validation results from a MY17 Bolt EV demonstrate the effectiveness and robustness of the proposed solution.

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Struwe, Weston, Edward Emmott, Melanie Bailey, Michal Sharon, Andrea Sinz, Fernando J. Corrales, Kostas Thalassinos, Julian Braybrook, Clare Mills, and Perdita Barran. "The COVID-19 MS Coalition—accelerating diagnostics, prognostics, and treatment." Lancet 395, no. 10239 (June 2020): 1761–62. http://dx.doi.org/10.1016/s0140-6736(20)31211-3.

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Oh, Jae Hyuk, Chang Gu Kim, and Young Man Cho. "Diagnostics and prognostics based on adaptive time-frequency feature discrimination." KSME International Journal 18, no. 9 (September 2004): 1537–48. http://dx.doi.org/10.1007/bf02990368.

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Cordoba-Arenas, Andrea, Jiyu Zhang, and Giorgio Rizzoni. "Diagnostics and Prognostics Needs and Requirements for Electrified Vehicles Powertrains." IFAC Proceedings Volumes 46, no. 21 (2013): 524–29. http://dx.doi.org/10.3182/20130904-4-jp-2042.00139.

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Atamuradov, Vepa, Kamal Medjaher, Fatih Camci, Noureddine Zerhouni, Pierre Dersin, and Benjamin Lamoureux. "Machine Health Indicator Construction Framework for Failure Diagnostics and Prognostics." Journal of Signal Processing Systems 92, no. 6 (January 3, 2020): 591–609. http://dx.doi.org/10.1007/s11265-019-01491-4.

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Luterotti, Svjetlana, Tončica Vukman Kordić, Ivka Zoričić Letoja, and Slavica Dodig. "Contribution to diagnostics/prognostics of tuberculosis in children. II. Indicative value of metal ions and biochemical parameters in serum." Acta Pharmaceutica 65, no. 3 (September 1, 2015): 321–29. http://dx.doi.org/10.1515/acph-2015-0027.

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AbstractNewly introduced methods of assaying simultaneously copper and zinc and zinc alone in serum by flame atomic-absorption spectrometry are simple and economical, especially in saving the consumption of serum material. Along with biochemical parameters, they have been successfully applied to diagnostics/prognostics of tuberculosis in children, through analyses of sera from pediatric patients with lung tuberculosis or suspected tuberculosis, enabling the follow-up of therapeutic efficiency. The prognostic strength of Cu and Cu/Zn ratio together with C-reactive protein, complement components C3 and C4, and erythrocyte sedimentation rate have been documented.

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Yadav, Ashish. "Exploring the Landscape of Gas Turbine Health Monitoring through Machine Learning: A Comprehensive Survey." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (March 13, 2024): 1–11. http://dx.doi.org/10.55041/ijsrem29237.

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Within today's fiercely competitive industrial landscape, effective condition monitoring, diagnostics, and prognostics play pivotal roles. The digitization of equipment has exponentially expanded data availability across industrial processes, driving the development of advanced techniques that significantly enhance machine performance. This paper delves into a decade- spanning survey of the evolution of condition monitoring, diagnostics, and prognostics, specifically focusing on machine learning (ML)-based approaches for optimizing gas turbine operational efficiency. Through an exhaustive literature review, this publication evaluates the performance of ML models and their applications in the realm of gas turbines. It also addresses key challenges and opportunities in gas turbine research. Ultimately, the synthesis of data collected from various sources coupled with ML techniques demonstrates promising potential in enhancing the accuracy, robustness, precision, and overall performance of industrial gas turbine systems.

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Figueroa Barraza, Joaquín, Enrique López Droguett, and Marcelo Ramos Martins. "Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks." Sensors 21, no. 17 (September 1, 2021): 5888. http://dx.doi.org/10.3390/s21175888.

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In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy/interpretability tradeoff, which means that their complexity leads to high performance levels but lacks interpretability. This work aims at addressing this tradeoff by proposing a technique for feature selection embedded in deep neural networks that uses a feature selection (FS) layer trained with the rest of the network to evaluate the input features’ importance. The importance values are used to determine which will be considered for deployment of a PHM model. For comparison with other techniques, this paper introduces a new metric called ranking quality score (RQS), that measures how performance evolves while following the corresponding ranking. The proposed framework is exemplified with three case studies involving health state diagnostics and prognostics and remaining useful life prediction. Results show that the proposed technique achieves higher RQS than the compared techniques, while maintaining the same performance level when compared to the same model but without an FS layer.

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Chang, Fu-Kuo, Johannes F. C. Markmiller, Jeong-Beom Ihn, and Kok Yen Cheng. "A Potential Link from Damage Diagnostics to Health Prognostics of Composites through Built-in Sensors." Journal of Vibration and Acoustics 129, no. 6 (September 29, 2006): 718–29. http://dx.doi.org/10.1115/1.2730530.

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This paper explores the potential of integration of damage diagnostics based on built-in sensors with a progressive failure modeling for monitoring and prediction of composite structures, from damage initiation to final failure while they are in service. A piezobased structural health monitoring system was utilized to monitor the damage initiation based on acoustic signals and to detect its growth based on ultrasonic waves generated by the piezoelectric sensors. Utilizing a damage index and an imaging algorithm, damage initiation and damage extent were estimated, respectively. A finite element code (ABAQUS) based on a progressive failure analysis was adopted to simulate damage initiation and propagation in composites under a given loading condition. The numerical data and the diagnostic images were compared to x-ray pictures of a test coupon to verify the results. The results of the study strongly indicate that damage diagnostics and health prognostics could potentially be integrated to produce a powerful tool for managing the operation of composite structures in service.

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BONISSONE, PIERO, and KAI GOEBEL. "Soft Computing for diagnostics in equipment service." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 4 (September 2001): 267–79. http://dx.doi.org/10.1017/s0890060401154028.

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We present methods and tools from the Soft Computing (SC) domain, which is used within the diagnostics and prognostics framework to accommodate imprecision of real systems. SC is an association of computing methodologies that includes as its principal members fuzzy, neural, evolutionary, and probabilistic computing. These methodologies enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. We outline the advantages and disadvantages of these methodologies and show how they can be combined to create synergistic hybrid SC systems. We conclude the paper with a description of successful SC case study applications to equipment diagnostics.

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de Castro-Cros, Martí, Manel Velasco, and Cecilio Angulo. "Machine-Learning-Based Condition Assessment of Gas Turbines—A Review." Energies 14, no. 24 (December 15, 2021): 8468. http://dx.doi.org/10.3390/en14248468.

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Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.

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Verdiyev, Safail. "Vibration prognostics and optimal diagnostics of chimneys at power generation facilities." Energy Safety and Energy Economy 3 (June 2018): 40–42. http://dx.doi.org/10.18635/2071-2219-2018-3-40-42.

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Journal articles on the topic 'Diagnostics and prognostics'

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