A Deep Learning Approach for Short Term Prediction of Industrial Plant Working Status

Bellini, Pierfrancesco; Cenni, Daniele; Palesi, Luciano Alessandro Ipsaro; Nesi, Paolo; Pantaleo, Gianni

doi:10.1109/bigdataservice52369.2021.00007

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Articles Shapley Additive Explanations (V-A1) [72], [96], [99], [117], [118], [130], [132] [36]- [38], [42], [66], [75]- [77], [120], [131], [134], [135] Local Interpretable Model-agnostic Explanations (V-A2) [35]- [38], [44], [50], [51], [54], [61], [66], [76], [84] Feature Importance (V-A3) [34], [54], [67], [85], [86], [93], [101], [115], [137], [139] Layer-wise Relevance Propagation (V-A4) [37], [44], [68], [87], [109], [116] Rule-based (V-A5) [65], [70], [71], [73] Class Activation Mapping (CAM) and Gradient-weighted CAM (V-B1) [37], [44],…”

Section: Methodsmentioning

confidence: 99%

“…[56]- [68] CMAPSS [69] Shapley Additive Explanations and Rule Based [35]- [37], [70]-[80] General Machine Faults and Failures [81] Feature Importance, Shapley Additive Explanation, Class Activation Mapping and Local Interpretable Modle-agnostic Explanation [42], [48], [82]- [87] Trains Feature Importance [34], [88]- [93] Gearboxes [94] Shapley Additive Explanations and Interpretable Filters [42], [45], [48], [95] Artificial Dataset Local Interpretable Modle-agnostic Explanation, Shapley Additive Explanation, Counterfactual and Surrogate [44], [96], [97] Hot [38], [70], [106] Lithium-ion Batteries [107] Layer-wise Relevance Propagation [37], [108], [109] Wind Turbines [110] Autoencoder-based Anomaly Root Cause Analysis and Sparse Networks [111], [112] Amusement Park Rides Depth-based Isolation Forrest Feature Importance and Accelerated Model-agnostic Explanations [113], [114] Particle Accelerators Layer-wise Relevance Propagation and Feature importance [115], [116] Chemical plant Shapley Additive Explanations [117], [118] Semi-conductors [119] Shapley Additive Explanations and Knowledgebased [120], [121] Aircraft Fuzzy…”

Section: Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Cummins,

Sommers,

Ramezani

et al. 2024

IEEE Access

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Predictive maintenance is a well studied collection of techniques that aims to prolong the life of a mechanical system by using artificial intelligence and machine learning to predict the optimal time to perform maintenance. The methods allow maintainers of systems and hardware to reduce financial and time costs of upkeep. As these methods are adopted for more serious and potentially life-threatening applications, the human operators need trust the predictive system. This attracts the field of Explainable AI (XAI) to introduce explainability and interpretability into the predictive system. XAI brings methods to the field of predictive maintenance that can amplify trust in the users while maintaining well-performing systems. This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. We categorize the different XPM methods into groups that follow the XAI literature. Additionally, we include current challenges and a discussion on future research directions in XPM.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Cummins,

Sommers,

Ramezani

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Following the completion of the training phase, the model is put to use for online fault detection. In conclude, the suggested model is used to analyze the well-known Tennessee Eastman process, and the results of using this model are shown [ 33 ].This is the case even if the sample has high inequality. This is exemplified by the fact that the method can still attain this degree of accuracy with some limitations.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0

Raju

Ramaswamy

Eid

et al. 2023

Sensors

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Artificial intelligence (AI) systems are increasingly used in corporate security measures to predict the status of assets and suggest appropriate procedures. These programs are also designed to reduce repair time. One way to create an efficient system is to integrate physical repair agents with a computerized management system to develop an intelligent system. To address this, there is a need for a new technique to assist operators in interacting with a predictive system using natural language. The system also uses double neural network convolutional models to analyze device data. For fault prioritization, a technique utilizing fuzzy logic is presented. This strategy ranks the flaws based on the harm or expense they produce. However, the method’s success relies on ongoing improvement in spoken language comprehension through language modification and query processing. To carry out this technique, a conversation-driven design is necessary. This type of learning relies on actual experiences with the assistants to provide efficient learning data for language and interaction models. These models can be trained to have more natural conversations. To improve accuracy, academics should construct and maintain publicly usable training sets to update word vectors. We proposed the model dataset (DS) with the Adam (AD) optimizer, Ridge Regression (RR) and Feature Mapping (FP). Our proposed algorithm has been coined with an appropriate acronym DSADRRFP. The same proposed approach aims to leverage each component’s benefits to enhance the predictive model’s overall performance and precision. This ensures the model is up-to-date and accurate. In conclusion, an AI system integrated with physical repair agents is a useful tool in corporate security measures. However, it needs to be refined to extract data from the operating system and to interact with users in a natural language. The system also needs to be constantly updated to improve accuracy.

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Comparing Techniques for Temporal Explainable Artificial Intelligence

Canti,

Collini,

Palesi

et al. 2024

2024 IEEE 10th International Conference on Big Data Computing Service and Machine Learning Applications (BigDataService)

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A Deep Learning Approach for Short Term Prediction of Industrial Plant Working Status

Cited by 3 publications

References 21 publications

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0

Comparing Techniques for Temporal Explainable Artificial Intelligence

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