A health indicator method for degradation detection of electronic products

Kumar, Sachin; Vichare, Nikhil; Dolev, Eli; Pecht, Michael

doi:10.1016/j.microrel.2011.09.030

Cited by 34 publications

(10 citation statements)

References 12 publications

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“…References Fault diagnostics [1,2,3,6,7,8,9,10,11,12,13,14,15,16,17,18,19] System design [1,13,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32] Human factors [1,20,32,33,34,35,36,37] Data management [1,20,37,38,39] 1 [1,26] are review papers on the NFF.…”

Section: Categorymentioning

confidence: 99%

No Fault Found events in maintenance engineering Part 1: Current trends, implications and organizational practices

Khan

Phillips

Jennions

et al. 2014

Reliability Engineering & System Safety

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This paper reviews the literature dealing with the nature and characteristics of no-fault found events within maintenance lines. Increasing systems complexities have seen a rise in the number of unknown faults that are being reported during operational service. Units tagged as 'No-Fault Found' are evidence that a serviceable component was removed, and attempts to troubleshoot the root cause have been unsuccessful. This scenario worsens when faults occurring at the component level are intermittent in nature. Here, the paper describes the prominent issues that have persisted across a variety of industrial applications and processes for decades. Some recent developments including standards, financial implications and safety concerns are highlighted.

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Section: Categorymentioning

confidence: 99%

No Fault Found events in maintenance engineering Part 1: Current trends, implications and organizational practices

Khan

Phillips

Jennions

et al. 2014

Reliability Engineering & System Safety

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“…Early detection of anomalies in complex electronic systems prevents unexpected failures and enhances performance and availability [1]. The complex architecture of electronics, the interdependency of component functionalities, and the miniaturization of most electronic systems make it difficult to detect and analyze anomalous behaviors [2].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Hidden Markov Model-based approach for anomaly detection in electronic systems

Dorj

Chen

Pecht

2013

2013 IEEE Aerospace Conference

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Early detection of anomalies in any system or component prevents impending failures and enhances performance and availability. The complex architecture of electronics, the interdependency of component functionalities, and the miniaturization of most electronic systems make it difficult to detect and analyze anomalous behaviors. A Hidden Markov Model-based classification technique determines unobservable hidden behaviors of complex and remotely inaccessible electronic systems using observable signals. This paper presents a data-driven approach for anomaly detection in electronic systems based on a Bayesian Hidden Markov Model classification technique. The posterior parameters of the Hidden Markov Models are estimated using the conjugate prior method. An application of the developed Bayesian Hidden Markov Model-based anomaly detection approach is presented for detecting anomalous behavior in Insulated Gate Bipolar Transistors using experimental data. The detection results illustrate that the developed anomaly detection approach can help detect anomalous behaviors in electronic systems, which can help prevent system downtime and catastrophic failures.

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“…When dealing with analysis of complex seismic processes, it needs to be pointed out that the MD calculation is sensitive to inter-variable changes in a multivariate system (Mahalanobis, 1930;Lattin et al, 2003) and that it takes into account the correlations between several variables providing information on the similarity or dissimilarity between the compared groups (Taguchi and Jugulum, 2002;Kumar et al, 2012).…”

Section: Methods Of Analysismentioning

confidence: 99%

Mahalanobis distance-based recognition of changes in the dynamics of a seismic process

Matcharashvili

Czechowski

Zhukova

2019

Nonlin. Processes Geophys.

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Abstract. In the present work, we aim to analyse the regularity of a seismic process based on its spatial, temporal, and energetic characteristics. Increments of cumulative times, increments of cumulative distances, and increments of cumulative seismic energies are calculated from an earthquake catalogue for southern California from 1975 to 2017. As the method of analysis, we use the multivariate Mahalanobis distance calculation, combined with a surrogate data testing procedure that is often used for the testing of non-linear structures in complex data sets. Before analysing the dynamical features of the seismic process, we tested the used approach for two different 3-D models in which the dynamical features were changed from more regular to more randomised conditions by adding a certain degree of noise. An analysis of the variability in the extent of regularity of the seismic process was carried out for different completeness magnitude thresholds. The results of our analysis show that in about a third of all the 50-data windows the original seismic process was indistinguishable from a random process based on its features of temporal, spatial, and energetic variability. It was shown that prior to the occurrence of strong earthquakes, mostly in periods of generation of relatively small earthquakes, the percentage of windows in which the seismic process is indistinguishable from a random process increases (to 60 %–80 %). During periods of aftershock activity, the process of small earthquake generation became regular in all of the windows considered, and thus was markedly different from the randomised catalogues. In some periods within the catalogue, the seismic process appeared to be closer to randomness, while in other cases it became closer to a regular behaviour. More specifically, in periods of relatively decreased earthquake generation activity (with low energy release), the seismic process appears to be random, while during periods of occurrence of strong events, followed by series of aftershocks, significant deviation from randomness is shown, i.e. the extent of regularity markedly increases. The period for which such deviation from random behaviour lasts depends on the amount of seismic energy released by the strong earthquake.

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A health indicator method for degradation detection of electronic products

Cited by 34 publications

References 12 publications

No Fault Found events in maintenance engineering Part 1: Current trends, implications and organizational practices

No Fault Found events in maintenance engineering Part 1: Current trends, implications and organizational practices

A Bayesian Hidden Markov Model-based approach for anomaly detection in electronic systems

Mahalanobis distance-based recognition of changes in the dynamics of a seismic process

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