Multi combination pattern labeling by using deep learning for chameleon rotary machine environment

Abstract

Abstract Rotary machines are constructed and operated in diverse industrial environments and operate according to various specifications and characteristics. When rotary machinery constructed under dynamic real world environments is in operation, various types of vibrations are generated depending on the normal or defective state of the machinery. In this way, Numerous studies have been conducted on vibration analysis for diagnosing the state of rotary machinery. However, Without performing robust data cleansing and comprehensive labeling of the internal and external state of complex machinery, the analysis process of the condition monitoring system faces difficulties in accurately identifying the various and complex states of rotary machines and making decisions in the dynamic real world. To overcome these limitations, this paper proposes Multi Combination Pattern Labeling (MCPL) method. By simultaneously considering the complex internal and external states of rotary machines, MCPL generates detailed vibration frequency pattern criteria and labels for each state. Based on these complex pattern classifications, it is able to classify various types of abnormal states. The MCPL generates FFT patterns and spectrogram patterns by considering the simultaneous internal and external states of the rotary machine. Extracting internal and external patterns, each pattern is combined for identifying convergence patterns, named MCP. Each MCP proceeds labeling process, named MCPL, then MCPL dataset is structured. MCPL dataset is verified based on Deep Neural Network (DNN) and Convolutional Neural Network (CNN). By utilizing the DNN and CNN techniques to derive MCPL from MCP, it becomes possible to perform unbiased state diagnosis across a variety of patterns, based on the complex patterns of the internal and external states of the rotating machinery. Presenting high accuracy and stable results, MCPL are able to classify rotary machine states and detect anomalies under the convergence environment. Our source code and utilized data are available on https://github.com/JEJESBSB/Journal-of-Big-Data .

Keywords

• Fast fourier fransform
• Short-time fourier ransform
• Spectrogram
• Deep learning
• Deep neural network
• Convolutional neural network