Distributed Attention-Based Temporal Convolutional Network for Remaining Useful Life Prediction

Yan, Shuicheng; Gao, Shengyao; Li, Yibin; Jia, Lixin; Li, Qiqiang; Pang, Fuzhen

doi:10.1109/jiot.2020.3004452

Cited by 99 publications

(26 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RMSE was utilized to examine the model's predictive performance with AU and EU [56]. To obtain a meaningful measure, the mean performance for 100 predictions was calculated.…”

Section: Model Performance Assesment and Shap Explainabilitymentioning

confidence: 99%

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

et al. 2022

View full text Add to dashboard Cite

Mistrust, amplified by numerous artificial intelligence (AI) related incidents, is an issue that has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and is fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle such an issue. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced into XAI. However, many deficiencies, particularly the lack of explanation assessment methods and uncertainty quantification, plague this young domain. In the present paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model and Shapley additive explanations (SHAP) to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as a marker for anomalies and expands the prognostic explanation scope to include the model’s confidence. In addition, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of studies on PHM-XAI. The quality of the explanation is examined employing local accuracy and consistency properties. The elaborated framework is tested on real-world gas turbine anomalies and synthetic turbofan failure prediction data. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed a 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

show abstract

“…The RMSE was utilized to examine the model's predictive performance with AU and EU [56]. To obtain a meaningful measure, the mean performance for 100 predictions was calculated.…”

Section: Model Performance Assesment and Shap Explainabilitymentioning

confidence: 99%

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The RMSE is utilized to examine the model's predictive performance with aleatoric and epistemic uncertainties [52]. In order to obtain a meaningful measure, the mean performance for 100 predictions is calculated.…”

Section: Model Performance Assesment and Shap Explainabilitymentioning

confidence: 99%

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study of Real-World Gas Turbine Anomalies

Nor¹,

Pedapati²,

Muhammad³

et al. 2022

Preprint

View full text Add to dashboard Cite

: Mistrust, amplified by numerous artificial intelligence (AI) related incidents, has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle this challenge. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced to XAI. However, many deficiencies, particularly lack of explanation assessment methods and uncertainty quantification, plague this young field. In this paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as marker for anomalies expanding the prognostic explanation scope to include model’s confidence. Also, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of PHM-XAI publications. The quality of the explanation is finally examined employing local accuracy and consistency properties. The method is tested on real-world gas turbine anomalies and synthetic turbofan data failure prediction. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

show abstract

“…Firstly, the sentence composed of a sequence of words is transformed into a word vector through a word embedding matrix, and then the hidden layer output h i is obtained through BiGRU network. Then a linear layer is transformed to obtain u i , using the softmax function to get the importance weight δ i of each word, and finally, the information representation of the sentence vector s is obtained by a weighted average of the output of BiGRU [34]. The formulas are as follows:…”

Section: Attention Mechanismmentioning

confidence: 99%

Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model

2021

View full text Add to dashboard Cite

The prerequisite for victory in war is the rapid and accurate identification of the tactical intention of the target on the battlefield. The efficiency of manual recognition of the combat intention of air targets is becoming less and less effective with the advent of information warfare. Moreover, if the traditional method of combat intention of air targets is based only on data from a single moment in time, the characteristic information on the time-series data is difficult to capture effectively. In this context, we design a new deep learning method attention mechanism with temporal convolutional network and bidirectional gated recurrent unit (Attention-TCN-BiGRU) to improve the recognition of the combat intent of air targets. Specifically, suitable characteristics are selected based on the combat mission and air posture to construct a characteristic set of air target intentions and encode them into temporal characteristics. Each characteristic in the characteristic set is given an appropriate weight through the attention mechanism. In addition, temporal convolutional network (TCN) is used to mine the data for latent characteristics and bidirectional gated recurrent unit (BiGRU) is used to capture long-term dependencies in the data. Experiments comparing with other methods and ablation demonstrate that Attention-TCN-BiGRU outperforms state-of-the-art methods in terms of accuracy in recognizing target intent in the air.

show abstract

Distributed Attention-Based Temporal Convolutional Network for Remaining Useful Life Prediction

Cited by 99 publications

References 21 publications

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study of Real-World Gas Turbine Anomalies

Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model

Contact Info

Product

Resources

About