Unsupervised Induction Motor Anomaly Detection Using a Deep Convolutional Autoencoder Based on Multi-Sensor Data Fusion

Induction motors are the primary way to convert electrical current into mechanical power. They are a fundamental component of industrial processes and equipment. Early fault detection and preventive maintenance are of great concern. In the last few years, many deep learning data-driven approaches have been used to detect faults in electric motors. This problem comes with two significant challenges: some faults are easier to detect using a specific sensor (e.g., vibration or current); in industrial applications, it is hard to obtain fault measurements. In most cases, only measurements of normal behaviour are available. This paper presents a multi-signal unsupervised anomaly detection system based on deep convolutional variational autoencoders (VAE). We use three sensors to sample from operating industrial motors: vibration, current, and magnetic flux. We divide the dataset into a training set, in which the network fits the nominal working condition of the motor. The system is then deployed in detection mode, analyzing the stream of data provided by the sensors. The experimental results show that the system accurately detects anomalies and has sufficient sensitivity to recognize changes in the motor load and behavior in practice.