Explainable AI based Fault Detection and Diagnosis System for Air Handling Units

Belikov, Juri; Meas, Molika; Machlev, Ram; Kose, Ahmet; Tepljakov, Aleksei; Loo, L; Petlenkov, Eduard; Levron, Yoash

doi:10.5220/0011350000003271

Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics 2022

DOI: 10.5220/0011350000003271

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Explainable AI based Fault Detection and Diagnosis System for Air Handling Units

Juri Belikov

Molika Meas

Ram Machlev

et al.

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State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies

Ma,

Flanigan,

Bergés

2024

Advanced Engineering Informatics

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State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies

Ma,

Flanigan,

Bergés

2024

Advanced Engineering Informatics

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Analysis of Statistical and Deep Learning Techniques for Temperature Forecasting

Ganesan Kruthika,

Rajasekaran,

Alagarsamy

et al. 2024

RACSC

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In the field of meteorology, temperature forecasting is a significant task as it has been a key factor in industrial, agricultural, renewable energy, and other sectors. High accuracy in temperature forecasting is needed for decision-making in advance. Since temperature varies over time and has been studied to have non-trivial long-range correlation, non-linear behavior, and seasonal variability, it is important to implement an appropriate methodology to forecast accurately. In this paper, we have reviewed the performance of statistical approaches such as AR and ARIMA with RNN, LSTM, GRU, and LSTM-RNN Deep Learning models. The models were tested for short-term temperature forecasting for a period of 48 hours. Among the statistical models, the AR model showed notable performance with a r2 score of 0.955 for triennial 1 and for the same, the Deep Learning models also performed nearly equal to that of the statistical models and thus hybrid LSTM-RNN model was tested. The hybrid model obtained the highest r2 score of 0.960. The difference in RMSE, MAE and r2 scores are not significantly different for both Statistical and Vanilla Deep Learning approaches. However, the hybrid model provided a better r2 score, and LIME explanations have been generated for the same in order to understand the dependencies over a point forecast. Based on the reviewed results, it can be concluded that for short-term forecasting, both Statistical and Deep Learning models perform nearly equally.

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Explainability and Transparency of Classifiers for Air-Handling Unit Faults Using Explainable Artificial Intelligence (XAI)

Meas

Machlev

Kose³

et al. 2022

Sensors

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In recent years, explainable artificial intelligence (XAI) techniques have been developed to improve the explainability, trust and transparency of machine learning models. This work presents a method that explains the outputs of an air-handling unit (AHU) faults classifier using a modified XAI technique, such that non-AI expert end-users who require justification for the diagnosis output can easily understand the reasoning behind the decision. The method operates as follows: First, an XGBoost algorithm is used to detect and classify potential faults in the heating and cooling coil valves, sensors, and the heat recovery of an air-handling unit. Second, an XAI-based SHAP technique is used to provide explanations, with a focus on the end-users, who are HVAC engineers. Then, relevant features are chosen based on user-selected feature sets and features with high attribution scores. Finally, a sliding window system is used to visualize the short history of these relevant features and provide explanations for the diagnosed faults in the observed time period. This study aimed to provide information not only about what occurs at the time of fault appearance, but also about how the fault occurred. Finally, the resulting explanations are evaluated by seven HVAC expert engineers. The proposed approach is validated using real data collected from a shopping mall.

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Explainable AI based Fault Detection and Diagnosis System for Air Handling Units

Cited by 3 publications

References 0 publications

State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies

State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies

Analysis of Statistical and Deep Learning Techniques for Temperature Forecasting

Explainability and Transparency of Classifiers for Air-Handling Unit Faults Using Explainable Artificial Intelligence (XAI)

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