Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals

Abstract

Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to the high currents flowing along those bars during start-up. In order to reduce the stresses that could lead to the appearance of these faults, the use of soft starters is becoming usual. However, these devices introduce additional components in the current and flux signals, affecting the evolution of the fault-related patterns and so making the fault diagnosis process more difficult. This paper proposes a new method to automatically classify the rotor health state in IMs driven by soft starters. The proposed method relies on obtaining the Persistence Spectrum (PS) of the start-up stray-flux signals. To obtain a proper dataset, Data Augmentation Techniques (DAT) are applied, adding Gaussian noise to the original signals. Then, these PS images are used to train a Convolutional Neural Network (CNN), in order to automatically classify the rotor health state, depending on the severity of the fault, namely: healthy motor, one broken bar and two broken bars. This method has been validated by means of a test bench consisting of a 1.1 kW IM driven by four different soft starters coupled to a DC motor. The results confirm the reliability of the proposed method, obtaining a classification rate of 100.00% when analyzing each model separately and 99.89% when all the models are analyzed at a time.

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning

“…In [35], the automatic detection of BRB faults in SCIM with soft start is investigated. The detection is tested with four different soft starters.…”

Machine learning for fault analysis in rotating machinery: A comprehensive review

“…It is seen in Fig. 5 that the majority of the studies are considered bearing faults [ [71] , [72] , [73] , [74] , [75] ] followed by rotor faults [ [76] , [77] , [78] , [79] , [80] ], gear faults [ [81] , [82] , [83] , [84] , [85] ], shaft faults [ 21 , 46 , 71 , 72 , 86 ], stator [ 7 , 25 , 87 ], and other component faults [ 28 , [88] , [89] , [90] ]. The pie chart shown in Fig.…”

Fault Voiceprint Signal Diagnosis Method of Power Transformer Based on Mixup Data Enhancement

“…In the final fully connected layer, the model still faces the problem of a large number of parameters; so, the pooling layer is required to reduce the number of image features. The purpose of the pooling layer is to reduce the convolution kernel to achieve the purpose of dimensionality reduction [ 24 , 25 , 26 , 27 ]. At present, there are maximum pooling and average pooling.…”