Improved deep-learning rotor fault diagnosis based on multi vibration sensors and recurrence plots

Deep learning techniques are increasingly applied to time series data, offering promising results in various fields. Deep learning techniques can handle data from multiple sensors to detect anomalies in an industrial environment. This paper proposes a new method of anomaly detection based on a multilayer image representation of different vibration sensors’ recurrence plots. Each sensor’s recurrence plot forms a layer. The performance and reliability of our method were assessed using an experimental database collected under different load conditions and with different types of rotor anomalies. Experiment results demonstrate the effectiveness of GoogLeNet using individual and multi-layered recurrence plots to find rotor faults in an induction motors.