Predictive and Explainable Machine Learning for Industrial Internet of Things Applications

Christou, Ioannis T.; Kefalakis, Nikos; Zalonis, Andreas; Soldatos, John

doi:10.1109/dcoss49796.2020.00043

Cited by 22 publications

(13 citation statements)

References 12 publications

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“…Zhao et al (2017) proposed using a Convolutional Bi-directional LSTM for fault prediction. Guo et al (2021) Decision Trees are widely applied to interpret a black box deep learning model through explanations by simplification approach (Christou et al, 2020;Mehdiyev & Fettke, 2021;Senoner et al, 2022). To this end, Senoner et al (2022) proposed a gradient boosting with decision trees to improve process quality and used SHAP values to obtain the importance of the features.…”

Section: Fault Diagnosis Methodsmentioning

confidence: 99%

“…Mehdiyev and Fettke (2021) proposed a technique for predictive maintenance; they also proposed a model agnostic explanation approach called Surrogate Decision Trees. Christou et al (2020) used a rule-based model to explain the results from a model used to estimate the remaining useful life of industrial equipment. Brito et al (2021) used a number of machine learning techniques along with the SHAP and Local Depth-based Feature Importance for the Isolation Forest.…”

Section: Fault Diagnosis Methodsmentioning

confidence: 99%

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Explainable fault prediction using learning fuzzy cognitive maps

Mansouri

Vadera

2023

Expert Systems

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IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify valuable insights for predictive preventive maintenance. One specific class of applications involves using Long Short‐Term Memory Networks (LSTMs) to predict faults happening in the near future. However, despite their remarkable performance, LSTMs can be very opaque. This paper deals with this issue by applying Learning Fuzzy Cognitive Maps (LFCMs) for developing simplified auxiliary models that can provide greater transparency. An LSTM model for predicting faults of industrial bearings based on readings from vibration sensors is developed to evaluate the idea. An LFCM is then used to imitate the performance of the baseline LSTM model. Through static and dynamic analyses, we demonstrate that LFCM can highlight (i) which members in a sequence of readings contribute to the prediction result and (ii) which values could be controlled to prevent possible faults. Moreover, we compare LFCM with state‐of‐the‐art methods reported in the literature, including decision trees and SHAP values. The experiments show that LFCM offers some advantages over these methods. Moreover, LFCM, by conducting a what‐if analysis, could provide more information about the black‐box model. To the best of our knowledge, this is the first time LFCMs have been used to simplify a deep learning model to offer greater explainability.

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Section: Fault Diagnosis Methodsmentioning

confidence: 99%

Section: Fault Diagnosis Methodsmentioning

confidence: 99%

Explainable fault prediction using learning fuzzy cognitive maps

Mansouri

Vadera

2023

Expert Systems

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“…Here, we will give particular attention to the quantitative association rule mining algorithm (QARMA), which has not yet been employed for the fault selection in transmission lines. QARMA has already been tested in several application scenarios and use-cases in the health domain and in predictive maintenance applications in particular (see [10,11] for results relating to predicting tool Remaining Useful Life in the auto-motive manufacturing industry from the recently concluded PROPHESY project). Within the context of the EU-funded QU4LITY 1 project, QARMA results have been tested against real-world data-sets ranging from tool wear-and-tear to body measurements to compute morphotype fit scores in the fashion industry.…”

Section: (A) (B)mentioning

confidence: 99%

Performance evaluation of machine learning for fault selection in power transmission lines

et al. 2022

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Learning methods have been increasingly used in power engineering to perform various tasks. In this paper, a fault selection procedure in double-circuit transmission lines employing different learning methods is accordingly proposed. In the proposed procedure, the discrete Fourier transform (DFT) is used to pre-process raw data from the transmission line before it is fed into the learning algorithm, which will detect and classify any fault based on a training period. The performance of different machine learning algorithms is then numerically compared through simulations. The comparison indicates that an artificial neural network (ANN) achieves remarkable accuracy of 98.47%. As a drawback, the ANN method cannot provide explainable results and is also not robust against noisy measurements. Subsequently, it is demonstrated that explainable results can be obtained with high accuracy by using rule-based learners such as the recently developed quantitative association rule mining algorithm (QARMA). The QARMA algorithm outperforms other explainable schemes, while attaining an accuracy of 98%. Besides, it was shown that QARMA leads to a very high accuracy of 97% for highly noisy data. The proposed method was also validated using data from an actual transmission line fault. In summary, the proposed two-step procedure using the DFT combined with either deep learning or rule-based algorithms can accurately and successfully perform fault selection tasks but indicating remarkable advantages of the QARMA due to its explainability and robustness against noise. Those aspects are extremely important if machine learning and other data-driven methods are to be employed in critical engineering applications.

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“…An overview of XAI for predictive maintenance in the aerospace area was presented in [ 37 ]. In [ 38 ] QARMA algorithm was introduced for predictive maintenance in industrial IoT applications. Instead of using black-box model and the explainability wrapper, the authors build glass-box rule-mining model that is inherently interpretable.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations

Jakubowski

Stanisz

Bobek

et al. 2021

Sensors

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Development of predictive maintenance (PdM) solutions is one of the key aspects of Industry 4.0. In recent years, more attention has been paid to data-driven techniques, which use machine learning to monitor the health of an industrial asset. The major issue in the implementation of PdM models is a lack of good quality labelled data. In the paper we present how unsupervised learning using a variational autoencoder may be used to monitor the wear of rolls in a hot strip mill, a part of a steel-making site. As an additional benchmark we use a simulated turbofan engine data set provided by NASA. We also use explainability methods in order to understand the model’s predictions. The results show that the variational autoencoder slightly outperforms the base autoencoder architecture in anomaly detection tasks. However, its performance on the real use-case does not make it a production-ready solution for industry and should be a matter of further research. Furthermore, the information obtained from the explainability model can increase the reliability of the proposed artificial intelligence-based solution.

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Predictive and Explainable Machine Learning for Industrial Internet of Things Applications

Cited by 22 publications

References 12 publications

Explainable fault prediction using learning fuzzy cognitive maps

Explainable fault prediction using learning fuzzy cognitive maps

Performance evaluation of machine learning for fault selection in power transmission lines

Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations

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