An Explainable Deep Learning-Based Predictive Maintenance Solution for Air Compressor Condition Monitoring

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 18; p. 5797
• Main Authors: Ciobotaru, Alexandru, Corches, Cosmina, Gota, Dan, Miclea, Liviu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.09.2025
• Subjects: Accuracy; Air conditioning; Algorithms; Artificial Intelligence; Care and treatment; condition monitoring; Control systems; Deep Learning; Equipment and supplies; Failure; Fault diagnosis; Health care industry; Heat recovery systems; Humans; HVAC; Laboratories; Laboratory equipment; LIME; Machine learning; Maintenance; Medical equipment; Neural networks; Neural Networks, Computer; Patients; Physiological apparatus; predictive maintenance; Preventive maintenance; Regression analysis; Sensors; SHAP; Support Vector Machine; SVM air compressor; Temperature; Useful life; Valves; Ventilation; deep learning; SHAP; LIME; predictive maintenance; condition monitoring; SVM air compressor; PDP
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25185797

Air compressors are vital across various sectors—automotive, manufacturing, buildings, and healthcare—as they provide pressurized air for air suspension systems in vehicles, supply power pneumatic machines throughout industrial production lines, and support non-clinical infrastructure within hospital environments, including pneumatic control systems, isolation room pressurization, and laboratory equipment operation. Ensuring that such components are reliable is critical, as unexpected failures can disrupt facility functions and compromise patient safety. Predictive maintenance (PdM) has emerged as a key factor in enhancing the reliability and operational efficiency of medical devices by leveraging sensor data and artificial intelligence (AI)-based algorithms to detect component degradation before functional failures occur. In this paper, a predictive maintenance solution for condition monitoring and fault prediction for the exhaust valve, bearings, water pump, and radiator of an air compressor is presented, by comparing a hybrid deep neural network (DNN) as a feature extractor and a support vector machine (SVM) for condition classification: a pure DNN classifier as well as a standalone SVM model. Additionally, each model was trained and validated on three devices—NVIDIA T4 GPU, Raspberry Pi 4 Model B, and NVIDIA Jetson Nano—and performance reports in terms of latency, energy consumption, and CO2 emissions are presented. Moreover, three model agnostic explainable AI (XAI) methods were employed to increase the transparency of the hybrid model’s final decision: Shapley additive explanations (SHAP), local interpretable model-agnostic explanations (LIME) and partial dependence plots (PDP). The hybrid model achieves on average 98.71%, 99.25%, 98.78%, and 99.01% performance in terms of accuracy, precision, recall, and F1-score across all devices Additionally, the DNN baseline and SVM model achieve on average 93.2%, 88.33%, 90.45%, and 89.37%, as well as 93.34%, 88.11%, 95. 41%, and 91.62% performance in terms of accuracy, precision, recall, and F1-score across all devices. The integration of XAI methods within the PdM pipeline offers enhanced transparency, interpretability, and trustworthiness of predictive outcomes, thereby facilitating informed decision-making among maintenance personnel.