Fault Detection of Induction Motors with Combined Modeling- and Machine-Learning-Based Framework

Abstract

This paper deals with the early detection of fault conditions in induction motors using a combined model- and machine-learning-based approach with flexible adaptation to individual motors. The method is based on analytical modeling in the form of a multiple coupled circuit model and a feedforward neural network. In addition, the differential evolution algorithm independently identifies the parameters of the motor for the multiple coupled circuit model based on easily obtained measurement data from a healthy state. With the identified parameters, the multiple coupled circuit model is used to perform dynamic simulations of the various fault cases of the specific induction motor. The simulation data set of the stator currents is used to train the neural network for classification of different stator, rotor, mechanical, and voltage supply faults. Finally, the combined method is successfully validated with measured data of faults in an induction motor, proving the transferability of the simulation-trained neural network to a real environment. Neglecting bearing faults, the fault cases from the validation data are classified with an accuracy of 94.81%.

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

“…To achieve rotor asymmetric fault detection, Marzebali et al proposed a method of mapping the static reference system obtained by the single stator current and Hilbert transform to a two-axis rotating reference system, and then used the synchronous squeezing wavelet transform for the time-frequency analysis of fault stator current under transient conditions [26]. Benninger et al proposed analytical modeling based on a multi-coupled circuit model and a feedforward neural network for IM fault detection [27]. Dehina et al proposed a detection algorithm based on the combination of a reduced Park vector modulus and discrete wavelet transform to obtain the value of single-phase stator current, reduce the computational complexity and extract the fault characteristic frequency [28].…”

A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines

“…Many sensors can collect signals and data for condition monitoring of IMs and fault detection. These sensors can measure various parameters such as voltage [9] and current [10][11][12], stray flux [13,14], singular value decomposition of the stator current [15], load torque analysis [16], neural network-based detection [17], etc. Some of these methods are invasive and rarely noticed.…”