Motor Fault Acoustic Detection System Based on Virtual Instrument

0 Introduction

In order to ensure the high quality of the products, each motor must be tested for parameters before leaving the factory. In today's large-scale production, the motor test line is currently the factory test method adopted by most motor manufacturers. Noise detection is one of the test items. The usual method is to let the test line pass through the anechoic chamber, and experienced workers identify the faulty motors by hearing in the room. This method has high requirements for operators, high labor intensity, lacks objectivity, cannot guarantee the stability of quality, and has a slow detection speed, which seriously affects the speed and accuracy of the motor factory test. Therefore, motor manufacturers urgently need to transform the existing noise factory detection technology.

At present, the most commonly used methods are vibration diagnosis technology and audio diagnosis technology. Vibration diagnosis technology is a contact measurement, which needs to overcome the influence of the test line body vibration, and the equipment structure is complex and the speed is slow; while audio diagnosis technology is a non-contact measurement. The equipment is simple and the speed is fast. For this reason, a motor fault acoustic detection system based on LabVIEW, a software platform of NI Company in the United States, was developed.

1 Composition of Virtual Instrument System

Virtual instrument is a popular instrument configuration and detection control scheme in the world today. Virtual instrument is an open system that combines a computer platform with hardware modules with standard interfaces and development and testing software. It has the characteristics of good versatility and ease of use. Its typical hardware structure is: sensor - signal conditioner - data acquisition device - computer. The overall structure of the motor fault acoustic detection system is shown in Figure 1, which consists of a monitoring head (microphone, amplification and protection circuit), an audio card and a computer [1].

Figure 1 Schematic diagram of the motor fault acoustic detection system

The monitoring head uses multiple microphones to pick up multi-point noise signals of the motor under test and convert the air vibration signals into electrical signals; the audio card uses a sound card to realize the mutual conversion between noise electrical signals (analog signals) and digital signals (WAV format); the computer records the WAV format digital signals and processes the waveform to determine whether any fault occurs.

2 Motor Fault Sound Detection Software System

LabVIEW is the originator of the concept of virtual instrument. This software platform integrates all the functions of hardware communication such as GPIB, VXL PXIRS-232 RS-485 and data acquisition card, and provides a large number of signal processing functions and signal analysis tools, which facilitates users to quickly and easily build virtual instrument test systems. Therefore, the software part of this system adopts the graphical software LabVIEW. The overall software structure block diagram of the system is shown in Figure 2.

Figure 2 Software block diagram of motor fault acoustic detection system [page]

The main tasks completed by the system software are:

(1) Display and record of motor noise signals;

(2) Signal analysis (including file analysis and real-time analysis) uses wavelet analysis and frequency domain analysis to alarm and display faults for abnormal signals:

(3) Saving and printing of documents;

(4) Motor and sensor parameter settings.

2.1 Signal Acquisition

This system uses a sound card as a noise collection tool. In terms of resolution, the general microcomputer multimedia sound card is 16 bits and the sampling frequency is 44.1/48kHz. Most mainstream mid-to-high-end sound cards have a sampling accuracy of 96kHz/24 bits, and some even reach 32 bits. The noise level and total harmonic distortion are higher, surpassing the indicators of most analog devices, and the price is relatively cheap. Therefore, it is feasible to use a sound card in the system.

LabVIEW provides a complete sound card control module. This article selects the "Sound Input" module. This module contains multiple functions to implement the settings, start, acquisition, stop and clear memory of the sound card. Figure 3 shows a block diagram of the sound acquisition program for a channel. The channel parameters in the system are set as follows: input is single channel, 16-bit sampling bit, 44.1kHz sampling frequency; output is 16-bit single channel. Figure 4 shows the noise signal of a motor with electromagnetic fault.

Figure 3 Sound collection program flowchart

Figure 4 Noise signal of a motor with electromagnetic fault

2.2 Signal Analysis

After the signal is collected, the program automatically processes and analyzes it. The power spectrum and 1/3 frequency spectrum of the noise signal are obtained by frequency domain analysis. This is convenient for test personnel to observe. Figure 4 is the power spectrum of the motor in Figure 3. The "Sound and Vibration" toolkit can be purchased with LabVIEW software, which can be used to easily perform various related analyses. Figure 5 shows the power spectrum of the noise of a motor with electromagnetic fault. [page]

Figure 5 Noise power spectrum of a motor with electromagnetic fault

The motor is a very complex mechanical system, and its noise signal contains rich information about the equipment status. Since the measured sound information contains various components and interference, it is a non-stationary signal. The traditional filtering method based on Fourier transform is contradictory in improving the signal-to-noise ratio and spatial resolution. Therefore, the system uses wavelet technology as a feature extraction tool. The main methods used are:

(1) Wavelet transform soft threshold denoising method. In the one-dimensional signal denoising algorithm, the most critical part is the selection of threshold and the quantization of threshold. The soft threshold denoising method can extract signal features more accurately.

(2) Arbitrary scale reconstruction of wavelet transform: continuous wavelet can be reconstructed at a selected scale to extract signal features.

In LabVIEW platform, you can use external signal processing toolkit to realize wavelet transform, or call Matlab or Matlab Script node in LabVIEW. Matlab Script node enables users to import Matlab program into flowchart and edit Matlab program in flowchart according to the syntax of Matlab program. In this way, users can use the powerful numerical calculation function of Matlab in LabVIEW.

When using Matlab script nodes, you must pay attention to the following: ① Matlab script nodes can only be used on Windows platforms; ② Matlab must be installed on the machine to use Matlab script nodes; ③ When combining LabVIEW and Matlab, you must pay attention to the matching of data types inside and outside the Matlab script node, otherwise errors or wrong information will be generated when LabVIEW is running.

Figure 6 shows the noise signal of the rear bearing scraping fault. The signal is denoised using the function in Matlab and the soft threshold filtering algorithm, and the denoising effect is shown in Figure 7. It can be seen that a good denoising effect is achieved in some sudden changes or peaks.

Figure 6 Noise signal of a rear bearing scraper motor

Figure 7 Signal of a rear bearing scraper motor after wavelet denoising

Determining the characteristic value of motor fault is one of the keys to fault diagnosis. The energy of the component signals in each frequency band contains a wealth of fault information, and the energy of certain frequency bands contains certain fault characteristics. This paper uses multi-resolution analysis to perform wavelet decomposition of the noise signal, and uses the energy characteristic value of each frequency band as the criterion to gradually perform fault diagnosis from low frequency to high frequency. Figure 8 shows the third-layer high-frequency coefficients of the wavelet decomposition of a motor with a bearing fault. From Figure 8, we can clearly observe the moment when the fault occurs, and we can also clearly capture the fault characteristic information of the noise signal in different frequency bands for feature extraction.

Figure 8 Wavelet decomposition coefficients of a motor with bearing failure

3 Conclusion

(1) The motor fault acoustic detection system is realized by using virtual instrument technology. The system hardware architecture is simple and has strong adaptability.

(2) The LabVIEW software platform is highly efficient;

(3) Apply wavelet analysis non-stationary signal processing technology to realize fault feature extraction, and practical application proves the effectiveness of this method;

(4) The Matlab Scrpt node method has powerful numerical calculation capabilities, but the execution speed will be reduced.