Prognostics and health monitoring of electronic systems

Lall, Pradeep; Lowe, Ryan; Goebel, Kai

doi:10.1109/esime.2011.5765855

Cited by 13 publications

(9 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers have applied deep learning technologies to PHM applications. Some focus on a subfield of PHM, e.g., fault diagnosis or prognosis [23], [24]; others focus on applications to a specific item, e.g., bearing or electronic system [25]- [27], while still others survey PHM applications from the point of view of various deep learning architectures [22], [28]. However, none provides a comprehensive survey of the full coverage of the PHM domain from an application perspective.…”

Section: Introductionmentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

View full text Add to dashboard Cite

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

show abstract

Section: Introductionmentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

View full text Add to dashboard Cite

show abstract

“…PHM at the component level directs the development of health monitoring strategies for specific components, such as electric motors, electronic devices, bearings, and gear reducers. It determines whether the health of the monitored component is time-degraded due to various environmental, operational, and performance-related parameters [2,3]. In contrast, PHM at the system level evaluates the detailed system health, factoring in system operation, design, and process-related parameters [4].…”

Section: Introductionmentioning

confidence: 99%

“…The success of the aforementioned approaches is highly dependent on the following factors: (1) data availability: if data are available, or can be acquired for specific components or systems; (2) data type: what type of data is known, or can be obtained, such as the vibration, acoustic emission, or electric current; (3) data quality: if data are recorded with precision, and they are constitutive of all the information required to analyze the features In recent years, the tremendous progress in artificial intelligence (AI) has strengthened the potential for designing PHM systems that are powerful enough to detect, diagnose, and predict faults at an earlier stage with high precision. Deep learning (DL) and machine learning (ML) have become essential tools in establishing the decision-making capabilities of a PHM system.…”

Section: Introductionmentioning

confidence: 99%

Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)

Rohan

2022

Mathematics

View full text Add to dashboard Cite

In the current Industry 4.0 revolution, prognostics and health management (PHM) is an emerging field of research. The difficulty of obtaining data from electromechanical systems in an industrial setting increases proportionally with the scale and accessibility of the automated industry, resulting in a less interpolated PHM system. To put it another way, the development of an accurate PHM system for each industrial system necessitates a unique dataset acquired under specified conditions. In most circumstances, obtaining this one-of-a-kind dataset is difficult, and the resulting dataset has a significant imbalance, a lack of certain useful information, and contains multi-domain knowledge. To address those issues, this paper provides a fault detection and diagnosis system that evaluates and preprocesses imbalanced, scarce, multi-domain (ISMD) data acquired from an industrial robot, utilizing signal processing (SP) techniques and deep learning-based (DL) domain knowledge transfer. The domain knowledge transfer is used to produce a synthetic dataset with a high interpolation rate that contains all the useful information about each domain. For domain knowledge transfer and data generation, continuous wavelet transform (CWT) with a generative adversarial network (GAN) was used, as well as a convolutional neural network (CNN), to test the suggested methodology using transfer learning and categorize several faults. The proposed methodology was tested on a real experimental bench that included an industrial robot created by Hyundai Robotics. This test had a satisfactory outcome with a 99.7% (highest) classification accuracy achieved by transfer learning on several CNN benchmark models.

show abstract

“…Traditionally, electronic failures were viewed as completely random in nature; however, significant research has looked at monitoring aging mechanisms and effects in recent years (Droste and Finklea 2006;Hofmeister et al 2006b;Vichare and Pecht 2006;Gu et al 2007;Kalgren et al 2007;Bailey et al 2011;Lall et al 2011). Electronic system prognostics on the board or circuit level commonly utilize a built-in self test (BIST) prognostic monitor or canary (Goodman 2000;Mishra et al 2002;Goodman et al 2006;Hofmeister et al 2006a).…”

Section: Electronic Prognosticsmentioning

confidence: 99%

Prognostics and Health Management in Nuclear Power Plants: A Review of Technologies and Applications

Coble¹,

Ramuhalli²,

Bond³

et al. 2012

View full text Add to dashboard Cite

Recent years have seen a resurgence of nuclear power worldwide, with interest in extending the operating life of the approximately 436 reactors currently in service (as of March, 2012), 61 new reactors being constructed, and as many as 162 under consideration. Renewed worldwide interest in nuclear power has been somewhat tempered by the March 2011 incident at Fukushima Dai-ichi in Japan. However, nuclear power is still considered a key element in meeting future worldwide sustainable energy, energy security, and emissions goals. Currently, three separate thrusts to safe and economical nuclear power development for energy security are being pursued in the United States: (i) longer term operation for the legacy fleet, from 40-60 and possibly 60-80 years; (ii) near-term new nuclear plants with a 60-year design life; and (iii) small modular reactors, which are expected to employ light water reactor technology at least in the medium term. Within these activities, attention is turning to enhanced methods for plant component and structural health management.The operating U.S. fleet includes 104 light water reactors. In addition, there are now (as of May 2012) four new nuclear power plants (AP-1000 plants) under construction in the United States, and two delayed plants are being completed by the Tennessee Valley Authority. There is also interest in the United States in small modular reactors (SMRs), which could be easier to match to existing grid infrastructure and which could replace aging coal fired plants. The current low price for natural gas presents a challenge to the economics of nuclear power, at least in the short term; however, some recent studies have demonstrated that nuclear generation will be competitive in the longer term (at least in some markets) when anticipated escalation in gas prices and the cost of building, operating, and maintaining gas-fired plants are considered over those same time periods.This report reviews the current state of the art of prognostics and health management (PHM) for nuclear power systems and related technology currently applied in field or under development in other technological application areas, as well as key research needs and technical gaps for increased use of PHM in nuclear power systems. The historical approach to monitoring and maintenance in nuclear power plants (NPPs), including the Maintenance Rule for active components and Aging Management Plans for passive components, are reviewed. An outline is given for the technical and economic challenges that make PHM attractive for both legacy plants through Light Water Reactor Sustainability (LWRS) and new plant designs. There is a general introduction to PHM systems for monitoring, fault detection and diagnostics, and prognostics in other, non-nuclear fields. The state of the art for health monitoring in nuclear power systems is reviewed. A discussion of related technologies that support the application of PHM systems in NPPs, including digital instrumentation and control systems, wired and wireless sensor techno...

show abstract

Prognostics and health monitoring of electronic systems

Cited by 13 publications

References 48 publications

A Review on Deep Learning Applications in Prognostics and Health Management

A Review on Deep Learning Applications in Prognostics and Health Management

Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)

Prognostics and Health Management in Nuclear Power Plants: A Review of Technologies and Applications

Contact Info

Product

Resources

About