Interpretable Convolutional Neural Network Through Layer-wise Relevance Propagation for Machine Fault Diagnosis

Grezmak, John; Zhang, Jianjing; Wang, Peng; Loparo, Kenneth A.; Gao, Robert X.

doi:10.1109/jsen.2019.2958787

Cited by 85 publications

(32 citation statements)

References 23 publications

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“…More recently, researchers have begun to focus on understanding DL mechanisms with the aim of facilitating the broad acceptance of the technique. An early work has been reported for DCNN-based motor diagnosis in which the layer-wise relevance propagation (LRP) has been investigated to visualize the frequency band that the DCNN is focused on when distinguishing different motor structural faults [113].…”

Section: Learning Features Frommentioning

confidence: 99%

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Arinez

Chang

Gao

et al. 2020

Journal of Manufacturing Science and Engineering

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240

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Today's manufacturing systems are becoming increasingly complex, dynamic and connected. The factory operation faces challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in Artificial Intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) Review the State-of-the-Art applications of AI to representative manufacturing problems, (2) Provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) Identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human robotic collaboration, process monitoring, diagnosis and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

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Section: Learning Features Frommentioning

confidence: 99%

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Arinez

Chang

Gao

et al. 2020

Journal of Manufacturing Science and Engineering

Self Cite

240

View full text Add to dashboard Cite

show abstract

“…In , CNN is employed with LRP to explain the diagnostic of gearbox failure. Again, in (Grezmak et al, 2020), CNN and LRP are utilize for fault classification and explanation of induction motor. In this work, the vibration time series data used as input is transformed into time-frequency image using Continuous Wavelet Transform (CWT) with Morlet wavelet.…”

Section: Related Literaturementioning

confidence: 99%

Failure Prognostic of Turbofan Engines with Uncertainty Quantification and Explainable AI (XIA)

Nor¹

2021

TURCOMAT

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Deep learning is quickly becoming essential to human ecosystem. However, the opacity of certain deep learning models poses a legal barrier in its adoption for greater purposes. Explainable AI (XAI) is a recent paradigm intended to tackle this issue. It explains the prediction mechanism produced by black box AI models, making it extremely practical for safety, security or financially important decision making. In another aspect, most deep learning studies are based on point estimate prediction with no measure of uncertainty which is vital for decision making. Obviously, these works are not suitable for real world applications. This paper presents a Remaining Useful Life (RUL) estimation problem for turbofan engines equipped with prognostic explainability and uncertainty quantification. A single input, multi outputs probabilistic Long Short-Term Memory (LSTM) is employed to predict the RULs distribution of the turbofans and SHapley Additive exPlanations (SHAP) approach is applied to explain the prognostic made. The explainable probabilistic LSTM is thus able to express its confidence in predicting and explains the produced estimation. The performance of the proposed method is comparable to several other published works

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“…While ML algorithms, such as neural network (NN) and support vector machine (SVM), have demonstrated high accuracy on several datasets, there is a significant issue called the “black-box” nature of their decision-making and learning processes. Because the learning process of black-box algorithms is neither transparent nor understandable to human operators, high accuracy on a given dataset may be misreading without a deeper understanding of causes from machine-related sensor inputs [ 13 ]. Therefore, interpretable ML-based models that can identify and analyze the root causes of fault detection in manufacturing have drawn attention from researchers.…”

Section: Backgroundsmentioning

confidence: 99%

Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

Jun

2021

Sensors

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Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level.

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Interpretable Convolutional Neural Network Through Layer-wise Relevance Propagation for Machine Fault Diagnosis

Cited by 85 publications

References 23 publications

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Failure Prognostic of Turbofan Engines with Uncertainty Quantification and Explainable AI (XIA)

Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

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