Design of a Fault Prediction Platform for Industrial Air Compressors

Abstract

Industrial air compressors serve as critical equipment in manufacturing environments, supplying compressed air essential for production processes. Unexpected failures in these systems can lead to severe operational disruptions, including production downtime, delivery delays, and increased emergency maintenance costs. in particular ,Small and medium-sized enterprises (SMEs), often lack access to high-cost diagnostic instruments and advanced monitoring infrastructure, resulting in continued reliance on periodic inspections. Under such conditions, gradual degradation frequently remains undetected, leading to unexpected failures or excessive preventive maintenance. This study aims to address these challenges by designing and implementing a lightweight fault-prediction platform for industrial air compressors using a low-cost ESP32 microcontroller (MCU) equipped with current, vibration, temperature, and pressure sensors. A total of ten sensors were installed on the target compressor, collecting data at one-second intervals. The collected signals were processed through averaging, absolute-value conversion, and offset correction to generate stable feature values. Based on these processed features, fault-related patterns associated with motor load, bearing condition, filter clogging, and lubrication state were evaluated using simple rule-based logic, enabling basic fault-signature detection without complex formulas or high-performance computing resources. In addition, multichannel sensor data were used to evaluate the applicability of data-driven fault-prediction algorithms, Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM), including Support Vector Machine (SVM), and Multivariate Vector Autoregression (VAR). Each sensor time series was normalized using mean and standard deviation to eliminate scale differences, and both normal and abnormal intervals were used to assess model feasibility. Visualization of pre- and post-normalization time-series data confirmed that the sensor signals were adequately transformed into a machine-learning-ready format. The research methodology consists of four main stages (1) reviewing compressor structure, fault characteristics, and condition-based maintenance (CBM) literature to determine monitoring targets. (2) designing the hardware architecture—including ESP32, sensor modules, power unit, and interface circuits—and implementing firmware for one-second data acquisition and preprocessing. (3) computing intuitive feature values such as current mean, vibration magnitude, and representative temperature and pressure values, and establishing a rule-based diagnostic logic using thresholds and reference levels. (4) deploying the platform on an actual industrial compressor, collecting operational data, validating platform behavior against maintenance logs, and applying machine-learning models to the same dataset to explore integration potential. Experimental results show that the ESP32-based platform reliably performed one-second data acquisition, preprocessing, and status evaluation in a factory environment. Variations in key sensor values—such as current, pressure, vibration, and temperature—enabled identification of major fault indicators, including increased motor load, suspected filter blockage, and potential bearing degradation. Data-driven analysis using normalized sensor data revealed changes in prediction errors and classification boundaries in intervals similar to those detected by the rule-based logic, demonstrating future applicability for platform enhancement. The main contribution of this study lies in presenting a practical, low-cost, and easily deployable fault-prediction platform capable of real-time monitoring and basic anomaly detection without expensive instrumentation or complex deep-learning models. Furthermore, by experimenting with SVM-, RNN-LSTM-, and VAR-based analyses on the same dataset, the study highlights the potential integration between lightweight embedded platforms and data-driven prognostic algorithms, suggesting future expansion toward TinyML and lightweight deep-learning-based predictive maintenance. Keywords: air compressor, predictive maintenance, condition-based maintenance, ESP32, sensor data, fault prediction platform