Open AccessResearch on Fault Diagnosis of Air Conditioner Based on Deep Learning
Author(s) -
Zhiting Liu,
Yuhua Wang,
Yuexia Zhou
Publication year - 2018
Publication title -
international journal of robotics and control
Language(s) - English
Resource type - Journals
eISSN - 2577-7769
pISSN - 2577-7742
DOI - 10.5430/ijrc.v2n1p18
Subject(s) - softmax function , artificial intelligence , hilbert–huang transform , feature extraction , pattern recognition (psychology) , fault (geology) , computer science , air conditioning , machine learning , unsupervised learning , support vector machine , feature vector , deep learning , engineering , filter (signal processing) , computer vision , mechanical engineering , seismology , geology
The essence of intelligent fault diagnosis is to classify the feature of faults by machine learning. It is difficult and key to extract fault characteristics of signals efficiently. The general feature extraction methods include time frequency domain feature extraction, Empirical Mode Decomposition (EMD), Wavelet Transform and Variational Mode Decomposition (VMD). However, these methods require a certain prior experience and require reasonable analysis and processing of the signals. In this paper, in order to effectively extract the fault characteristics of the air conditioner's vibration signal, the stacked automatic encoder (SAE) is used to extract the feature of air conditioner’s vibration signal, and the Softmax function is used to identify the air conditioner's working condition. The SAE performs unsupervised learning on the signal, and Softmax function performs supervised learning on the signal. The number of hidden layers and the number of hidden layer's nodes are determined through experiments. The effects of learning rate, learning rate decay, regularization, dropout, and batch size on the correct rate of the model in supervised learning and unsupervised learning are analyzed. Thereby realizing the fault diagnosis of the air conditioner. The recognition correct rate of deep learning model reached 99.92\%. The deep learning fault diagnosis method proposed in this paper is compared with EMD and SVM, VMD and SVM two kind of fault diagnosis methods.