Health condition monitoring of a complex hydraulic system using Deep Neural Network and DeepSHAP explainable XAI

Keleko, Aurelien Teguede; Kamsu-Foguem, Bernard; Ngouna, Raymond Houé; Tongne, Amèvi

doi:10.1016/j.advengsoft.2022.103339

Cited by 31 publications

(13 citation statements)

References 53 publications

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“…Amarasinghe et al [21] propose a framework for deep neural network (DNN)-based anomaly detection and a post hoc explanation generated by LRP. Similarly, condition monitoring of hydraulic systems has been proposed using a framework that combines a DNN with DeepSHAP for interpretability [22]. An XAI-based chiller fault detection and diagnosis is presented in [23].…”

Section: Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Moosavi,

Razavi-Far,

Palade

et al. 2024

Electronics

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For over a century, induction furnaces have been used in the core of foundries for metal melting and heating. They provide high melting/heating rates with optimal efficiency. The occurrence of faults not only imposes safety risks but also reduces productivity due to unscheduled shutdowns. The problem of diagnosing faults in induction furnaces has not yet been studied, and this work is the first to propose a data-driven framework for diagnosing faults in this application. This paper presents a deep neural network framework for diagnosing electrical faults by measuring real-time electrical parameters at the supply side. Experimental and sensory measurements are collected from multiple energy analyzer devices installed in the foundry. Next, a semi-supervised learning approach, known as the local outlier factor, has been used to discriminate normal and faulty samples from each other and label the data samples. Then, a deep neural network is trained with the collected labeled samples. The performance of the developed model is compared with several state-of-the-art techniques in terms of various performance metrics. The results demonstrate the superior performance of the selected deep neural network model over other classifiers, with an average F-measure of 0.9187. Due to the black box nature of the constructed neural network, the model predictions are interpreted by Shapley additive explanations and local interpretable model-agnostic explanations. The interpretability analysis reveals that classified faults are closely linked to variations in odd voltage/current harmonics of order 3, 11, 13, and 17, highlighting the critical impact of these parameters on the model’s prediction.

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Section: Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Moosavi,

Razavi-Far,

Palade

et al. 2024

Electronics

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“…The results confirmed higher accuracy (98.2-98.9%, average 98.7%) and better robustness of Convolutional-Bayesian network in fault diagnosis. Keleko [49] proposed a concept of a detailed framework for Condition Monitoring of complex hydraulic systems and multi-sensor data based on a deep neural network and other technologies such as Deep SHapley Additive exPlanations to ensure the reliability of the results. Ebrahimzadeh [50] introduced a new method based on thermalhydraulic simulation and feed-forward neural networks (FFNN) for detecting faults of sensors in nuclear power plants.…”

Section: Neural Parameter Monitoring and Fault Diagnosismentioning

confidence: 99%

Artificial Intelligence Methods in Hydraulic System Design

Filo

2023

Energies

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Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.

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“…In order to forecast the system's true operational states, Keleko et al [24] took a data-driven strategy, focusing on the Deep Neural Networks' (DNN) multi-class classification for unbalanced data. Although DNNs perform well, there are still some unanswered problems about how trustworthy they will be as "black box" models in more complicated applications, especially with regards to the decision-making processes and the potential ethical, economical, and transparent consequences on stakeholders.…”

Section: Related Studiesmentioning

confidence: 99%

Smart Health Monitoring Using Deep Learning and Artificial Intelligence

Philip,

Gandhimathi,

Chalichalamala

et al. 2023

RIA

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The genesis and spread of illnesses are a major concern in today's rapidly developing technological and evolutionary environment. The prevention and management of illnesses using technological means have emerged as one of the most pressing challenges facing the medical community. With today's hectic schedules, it's nearly impossible to stick to a healthy routine. The problems above can be fixed by using a smart health monitoring system. Two of the most rapidly emerging technologies are the Internet of Things (IoT) and artificial intelligence (AI). As more people relocate to urban areas, the idea of a "smart city" has become increasingly commonplace. Increased efficiency, decreased expenses, and a renewed emphasis on improving the quality of care provided to patients are central to the idea of a "smart city." There has to be a thorough familiarity with the various smart city frameworks before the Internet of Things (IoT) and artificial intelligence (AI) can be effectively used for remote healthcare monitoring (RHM) systems. Technologies, gadgets, systems, models, designs, use cases, and applications are all examples of frameworks. The RHM system, based on the Internet of Things, relies heavily on artificial intelligence (AI) and deep learning (DL) to analyse the data it collects. However, DL techniques are widely utilised for making analytical representations, and they are included in CDSS and other kinds of healthcare services. Patients are given personalised recommendations for therapy, lifestyle changes, and care plans by clinical decision support systems after each element is thoroughly analysed. Supporting healthcare applications, this technology may assess activities, etc. In light of this, this paper presents a survey that zeroes in on the best smart city applications for the Internet of Things in health. By analysing the most important monitoring applications across many models using appropriate IoT-based sensors, this research provides a comprehensive assessment of the technologies and systems involved in providing RHM services. Finally, this study makes a contribution to scientific understanding by identifying the primary constraints on this field of study and suggesting directions for further investigation.

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Health condition monitoring of a complex hydraulic system using Deep Neural Network and DeepSHAP explainable XAI

Cited by 31 publications

References 53 publications

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Artificial Intelligence Methods in Hydraulic System Design

Smart Health Monitoring Using Deep Learning and Artificial Intelligence

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