Condition Monitor System for Rotation Machine by CNN with Recurrence Plot

Abstract

Induction motors face various stresses under operating conditions leading to some failure modes. Hence, health monitoring for motors becomes essential. In this paper, we introduce an effective framework for fault diagnosis of 3-phase induction motors. The proposed framework mainly consists of two parts. The first part explains the preprocessing method, in which the time-series data signals are converted into two-dimensional (2D) images. The preprocessing method generates recurrence plots (RP), which represent the transformation of time-series data such as 3-phase current signals into 2D texture images. The second part of the paper explains how the proposed convolutional neural network (CNN) extracts the robust features to diagnose the induction motor’s fault conditions by classifying the images. The generated RP images are considered as input for the proposed CNN in the texture image recognition task. The proposed framework is tested on the dataset collected from different 3-phase induction motors working with different failure modes. The experimental results of the proposed framework show its competitive performance over traditional methodologies and other machine learning methods.

A Novel 2-D Current Signal-Based Residual Learning With Optimized Softmax to Identify Faults in Ball Screw Actuators

“…Various approaches have been used for signal transformations including continuous wavelet transform (CWT) [28], Recurrence plots which transform time signal data into 2D texture images [29], Hilbert-Huang transform [30] and combination of different transforms. This paper presents an effective approach for signal data preprocessing that converts 1-D time domain raw signal data into 2-D gray image.…”

New Spectral Markers for Broken Bars Diagnostics in Induction Motors

“…One can see the simulation of induction motor 4A132S4 with 25% fault rotor bar in Figure 1. The simulation of breakage bars in a short-circuited rotor winding was performed for the engine 4A132S4 [4], which possessed the following parameters: In this simulation, we introduced the fault in the rotor bars by reducing their active sections. To compute the stationary state of the faulted machine we used the transient simulation mode, obtaining the stator phase current as a result.…”

“…Hsueh et al proposed an approach for condition monitoring based on the combination of recurrence analysis and neural networks used to interpret recurrence data [4]. In the first stage, 3-phase current signals were transformed into 2D texture images.…”

An Intelligent Hybrid Scheme for Identification of Faults in Industrial Ball Screw Linear Motion Systems

“…Different techniques have been utilized for signal to image transformation including Hilbert-Huang transform [44], Recurrence plots to transform 1-D signal to 2-D texture image [45], Wavelet Transformation [49], [50], signal to gray image conversion using energy values [51], etc. Wavelet Transform gives time-frequency analysis by decomposing input signal into a family of wavelet components; each one with a resolution according to corresponding scale.…”