Fault Diagnosis Of The Key Components Of CNC Machine Based On Wavelet Neural Network

As a typical electromechanical integration equipment, CNC machine occupies an important position of modern machinery manufacturing industry, the number of its ownership has become a standard to measure the manufacturing capability of a regional, and its application is continuing to promote, so the fault diagnosis technology of CNC machine is especially important.As a conventional feed drive components of CNC machine, ball screw has a great impact on the processing performance of the machine. Its tiny abnormality may result in the fail of parts processed, and even affect the overall processing of the product, so it's an important condition of ensuring CNC machine's normal operation to monitor the condition of ball screw and detect its faults timely.With the comprehensive analysis capability in time domain and frequency domain, wavelet analysis is very important in the area of signal analysis. It overcomes the Fourier transform's weakness of partial analysis in the frequency domain, it combines the time-domain with frequency-domain, so the signal characteristics can be accurate descried. Wavelet packet analysis is an extension of wavelet analysis, it analyzes the high-frequency signal which is ignored in wavelet analysis, so its result is more complete.The wavelet neural network in this paper introduced wavelet into neural network, it replaced the activation function of artificial neuron with wavelet function to lead the characteristics of multi-scale into neural network. Thereby the wavelet neural network not only has the multi-resolution characteristics of wavelet but also keep the self-learning and pattern recognition capabilities of neural network.In this paper, the wavelet neural network is based on BP neural network. It introduced morlet wavelet function as the activation function of hidden layer neuron, which erected wavelet neural network. The simulation using sine function has confirmed the feasibility of the wavelet neural network, that is the wavelet neural network established can be used for pattern recognition.In this paper, the vibration signal of the ball screw is collected and analyzed, and the effective features which can express the condition of the ball screw are extracted from the time domain, frequency domain and time-frequency domain, then put these features into the WNN established to be trained and pattern recognized. The results confirmed the practicability of fault diagnosis of CNC using WNN. And By comparison with the BP neural network, the advantages of high stability and recognition rate are highlighted.The most important part in this paper is that a new type of wavelet neural network is established by introduce the wavelet function into the BP network successfully based on the study of algorithm and application of BP neural network. And the vibration signal of the ball screw is recognized successfully by the wavelet neural network so that the fault state of the ball screw is diagnosed correctly.