Single and Multi-label Fault Classification in rotors from unprocessed multi-sensor data through deep and parallel CNN architectures

摘要

An attempt has been made in this study to address two issues related to fault classification in machinery. The first concerns the need of pre-processing raw data collected by sensors and the other is identification in cases where more than one fault exist simultaneously, i.e. where multi-label faults are present. We describe a Convolution Neural Network (CNN) based Deep architecture for classification of Single faults from raw time domain data and a similar but shallower Parallel Multiple Binary Classifier Network for Multi-label Fault Classification. Data from previously conducted experiments on a Rotor-Bearing Fault Simulator are used. In the case of Single-Label fault classification, Support Vector Machines (SVMs), Clustering, Artificial Neural Networks (ANNs) and other algorithms have been used in the past . These procedures require the raw time domain data collected by sensors, to be first processed and handcrafted into parameters like Fast Fourier Transform (FFT) coefficients, Statistical Moments, etc. before being fed as inputs. Usage of ANN with raw time domain data is inadequate as it suffers from the vanishing gradient problem. We propose a Deep Learning Multi-channel Convolutional Neural Network (McCNN) architecture here, which eliminates this problem and employs the RGB image analogy for channelizing raw time-domain vibration signals from various sub-systems of the rotor-bearing installation. It is shown that this architecture effectively works on raw time-domain data and recognizes all kinds of Single-Label Faults. For Multi-Label faults also, which generally get classified as erroneous Single-Label type through routine codes, the parallel architecture is demonstrated to give good results.