The Application of AI/ML and IIoT to Predictive Maintenance

Abstract

Predictive maintenance has emerged as a critical approach to industrial automation, particularly as a part of the Industrial Internet of Things (IIoT) where real-time monitoring and smart analytics are key to reducing downtime and maximizing operational performance. The fault-tolerant predictive maintenance framework of air compressors based on probabilistic Hidden Markov Model (HMM) combined with IIoT-based sensor data is introduced in this paper. In the suggested model, these parameters are critical variables that are monitored to identify any onset of degradation and thus predict possible failures: vibration, temperature, pressure, acoustic signals and motor current. The system identifies the compressor conditions in six operational states (S₀–S₅), which makes it possible to dynamically make decisions and plan maintenance activities. The comparative performance examination demonstrates that the suggested HMM model has higher accuracy (96.3%), precision (95.6%), and recall (94.9%) in contrast to conventional approaches, Random Forest (93.1%), and LSTM (95.2%) models. It also gives a better lead time to failure identification (7.8 hours), 22.4 percent downtime reduction, and 18.7 percent maintenance cost savings. The uncertainty quantification and Remaining Useful Life (RUL) estimation are also considered in the model, which can ensure its robustness in face of sensor anomalies. The study proves that the application of machine learning, together with the IIoT infrastructure, enables a significant enhancement of predictive maintenance of industrial air compressor systems in order to ensure a higher level of reliability, a reduction of operational
costs, and more intelligent asset management.