Reliability engineering applications in electronic, software, nuclear and aerospace industries: A 20 year review (2000–2020)

Nor, Ahmad Kamal Mohd; Pedapati, Srinivasa Rao; Muhammad, Masdi

doi:10.1016/j.asej.2021.02.015

Cited by 23 publications

(15 citation statements)

References 106 publications

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“…Anomaly detection is the action of identifying unusual patterns going against the norm of operational indicators whereas diagnostic is the action of classifying failure and discovering the detailed root cause of failure. AI-based methods occupy a key position in PHM research as shown in [6]. XAI, on the other hand, is somewhat a novelty in PHM.…”

Section: The State Xai In Phmmentioning

confidence: 99%

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Application of Explainable AI (Xai) For Anomaly Detection and Prognostic of Gas Turbines with Uncertainty Quantification.

Nor¹,

Pedapati²,

Muhammad³

2021

Preprint

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XAI is presently in its early assimilation phase in Prognostic and Health Management (PHM) domain. However, the handful of PHM-XAI articles suffer from various deficiencies, amongst others, lack of uncertainty quantification and explanation evaluation metric. This paper proposes an anomaly detection and prognostic of gas turbines using Bayesian deep learning (DL) model with SHapley Additive exPlanations (SHAP). SHAP was not only applied to explain both tasks, but also to improve the prognostic performance, the latter trait being left undocumented in the previous PHM-XAI works. Uncertainty measure serves to broaden explanation scope and was also exploited as anomaly indicator. Real gas turbine data was tested for the anomaly detection task while NASA CMAPSS turbofan datasets were used for prognostic. The generated explanation was evaluated using two metrics: Local Accuracy and Consistency. All anomalies were successfully detected thanks to the uncertainty indicator. Meanwhile, the turbofan prognostic results show up to 9% improvement in RMSE and 43% enhancement in early prognostic due to SHAP, making it comparable to the best published methods in the problem. XAI and uncertainty quantification offer a comprehensive explanation package, assisting decision making. Additionally, SHAP ability in boosting PHM performance solidifies its worth in AI-based reliability research.

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Section: The State Xai In Phmmentioning

confidence: 99%

“…AI penetration is not limited to social media, where it is probably more visible to the general public, but it reaches far into niche areas. Much progress has been felt especially in fields such as healthcare [1], defense [2], manufacturing [3], biology [4] robotics [5] and reliability [6] in the recent years.…”

Section: Introductionmentioning

confidence: 99%

Application of Explainable AI (Xai) For Anomaly Detection and Prognostic of Gas Turbines with Uncertainty Quantification.

Nor¹,

Pedapati²,

Muhammad³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…AI penetration is not limited to social media, where it is probably more visible to the public, but it reaches far into niche areas. Much progress has been felt in recent years, especially in fields such as healthcare [2], defense [3], manufacturing [4], biology [5], robotics [6], and reliability [7].…”

Section: Artificial Intelligencementioning

confidence: 99%

“…In contrast, diagnostic is the action of classifying failure and discovering the detailed root cause of failure. AI-based methods occupy a crucial position in PHM research, as shown in [7]. In addition, the XAI is somewhat a novelty in PHM.…”

Section: The State Xai In Prognostic and Health Managementmentioning

confidence: 99%

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Explainable Artificial Intelligence for Anomaly Detection and Prognostic of Gas Turbines using Uncertainty Quantification with Sensor-Related Data

Nor¹,

Pedapati²,

Muhammad³

et al. 2021

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Explainable artificial intelligence (XAI) is in its assimilation phase in the prognostic and health management (PHM). The literature on PHM-XAI is deficient with respect to metrics of uncertainty quantification and explanation evaluation. This paper proposes a new method of anomaly detection and prognostic for gas turbines using Bayesian deep learning and Shapley additive explanations (SHAP). The method explains the anomaly detection and prognostic and improves the performance of the prognostic, aspects that have not been considered in the literature of PHM-XAI. The uncertainty measures considered serve to broaden explanation scope and can also be exploited as anomaly indicators. Real-world gas turbine sensor-related data are tested for the anomaly detection, while NASA commercial modular aero-propulsion system simulation data, related to turbofan sensors, were used for prognostic. The generated explanation is evaluated using two metrics: consistency and local accuracy. All anomalies were successfully detected using the uncertainty indicators. Meanwhile, the turbofan prognostic results showed up to 9% improvement in root mean square error and 43% enhancement in early prognostic due to the SHAP, making it comparable to the best existing methods. The XAI and uncertainty quantification offer a comprehensive explanation for assisting decision-making. Additionally, the SHAP ability to increase PHM performance confirms its value in AI-based reliability research.

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Discussion of “Specifying prior distributions in reliability applications”: Towards new formal rules for informative prior elicitation?

Bousquet¹

2023

Appl Stoch Models Bus & Ind

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SummaryThe article by Tian et al. (Appl. Stoch. Models Bus. Ind. 2023) takes an interesting look at the use of non‐informative priors adapted to several censoring processes, which are common in reliability. It proposes a continuum of modelling approaches that go as far as defining weakly informative priors to overcome the well‐known shortcomings of frequentist approaches to problems involving highly censored samples. In this commentary, I make some critical remarks and propose to link this work to a more generic vision of what could be a relevant Bayesian elicitation in reliability, taking advantage of recent theoretical and applied advances. Through tools like approximate posterior priors and prior equivalent sample sizes, and by illustrating them with simple reliability models, I suggest methodological avenues to formalize the elicitation of informative priors in a auditable, defensible way. By allowing a clear modulation of subjective information, this might respond to the authors' primary concern of constructing weakly informative priors and to a more general concern for precaution in Bayesian reliability.

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Reliability engineering applications in electronic, software, nuclear and aerospace industries: A 20 year review (2000–2020)

Cited by 23 publications

References 106 publications

Application of Explainable AI (Xai) For Anomaly Detection and Prognostic of Gas Turbines with Uncertainty Quantification.

Application of Explainable AI (Xai) For Anomaly Detection and Prognostic of Gas Turbines with Uncertainty Quantification.

Explainable Artificial Intelligence for Anomaly Detection and Prognostic of Gas Turbines using Uncertainty Quantification with Sensor-Related Data

Discussion of “Specifying prior distributions in reliability applications”: Towards new formal rules for informative prior elicitation?

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