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Data Clustering and Prediction for Fault Detection and Diagnostics
1988 American Control Conference, 1988The characterization of a data cluster representing a certain process behavior is achieved by developing steady-state nonlinear modeling of one or more critical signals as a function of other process variables in the system. This prediction model is used to detect either sensor maloperation or process anomaly by comparing the prediction and measurement
B. R. Upadhyaya, G. Mathai, J. D. Green
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The Research on the Diagnostics of the Overheat Detection System Fault
2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 2017Based on the requirements of a certain type of regional aircraft, the overheat detection system is designed to monitor bleed air leakage of the engines and APU (Auxiliary Power Unit). The detection system contains a series of overheat sensing elements, each of which is an inconel tube with center wire conductor embedded in the eutectic salt.
Tianshi Han, Xin Liu
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Multi-Modal Diagnostics for Vehicle Fault Detection
Dynamic Systems and Control, 2001Abstract On-board vehicle diagnostic systems must have low development and hardware costs in order to be viable. Model-based methods have shown promise since they use analytical redundancy to reduce costly physical redundancy. However, these methods must also be computationally efficient and function accurately even with simple, low-cost
Matthew L. Schwall, J. Christian Gerdes
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Diagnostic Circuit for Latent Fault Detection in SRAM Row Decoder
2020 21st International Symposium on Quality Electronic Design (ISQED), 2020Functional safety is crucial in automotive life-critical systems. Early diagnosis of unanticipated faults and failures is necessary to prevent hazardous implications. ISO 26262, Functional Safety-Road Vehicles, is an automotive industry-specific functional safety standard that describes the course for classification, detection and control of potential ...
Shivendra Singh +3 more
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Intelligent Fault Detection and Diagnostics
2017This chapter contains the last part of the research methodology. On the bases of the methods discussed in Chaps. 3 and 4, it develops the planned FDD system.
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Detection and modeling of acoustic emissions for fault diagnostics
Proceedings of 8th Workshop on Statistical Signal and Array Processing, 2002The formation of microcracks in a material creates propagating ultrasonic waves that are called acoustic emissions (AEs). These AEs provide an early warning to the onset of material failure. In practical cases, however, these AEs have to be detected at very low SNRs, amongst strong interference and random noise.
D. West +4 more
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Data-Driven Approach for Fault Detection and Diagnostic in Semiconductor Manufacturing
IEEE Transactions on Automation Science and Engineering, 2020Fault detection and classification (FDC) is important for semiconductor manufacturing to monitor equipment’s condition and examine the potential cause of the fault. Each equipment in the semiconductor manufacturing process is often accompanied by a large amount of sensor readings, also called status variable identification (SVID).
Shu-Kai S. Fan +4 more
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Dashboard: Nonintrusive Electromechanical Fault Detection and Diagnostics
2019 IEEE AUTOTESTCON, 2019Modern power monitoring systems record vast amounts of equipment operational data. For these systems to improve efficiency and performance, the data must be presented as an intuitive decision aid for watchstanders. The Nonintrusive Load Monitor (NILM) dashboard provides actionable information for energy scorekeeping, activity tracking, and equipment ...
Daisy Green +5 more
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Model Uncertainity, Fault Detection and Diagnostics
2017The previous chapter has explained the concepts behind NF based model identification and how it relates to other models and the design in the framework of OBFs. It was stated that a good nonlinear model can be developed from plant operation data or a simulated output without knowing the model structure.
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Fault Detection and Diagnostics Using Data Mining
2014The purpose of data mining is to find new knowledge from databases in which complexity or the amount of data has so far been prohibitively large for human observation alone. Self-Organizing Map (SOM) is a special type of Artificial Neural Networks (ANNs) used in clustering, visualization and abstraction.
Sun Chung, Dukki Chung
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