Abstract
Installation of a Wireless and Powerless Sensing Node (WPSN) inside a spindle enables the direct transmission of monitoring signals through a metal case of a certain thickness instead of the traditional method of using connecting cables. Thus, the node can be conveniently installed inside motors to measure various operational parameters. This study extends this earlier finding by applying this advantage to the monitoring of spindle systems. After over 2 years of system observation and optimization, the system has been verified to be superior to traditional methods. The innovation of fault diagnosis in this study includes the unmatched assembly dimensions of the spindle system, the unbalanced system, and bearing damage. The results of the experiment demonstrate that the WPSN provides a desirable signal-to-noise ratio (SNR) in all three of the simulated faults, with the difference of SNR reaching a maximum of 8.6 dB. Following multiple repetitions of the three experiment types, 80% of the faults were diagnosed when the spindle revolved at 4,000 rpm, significantly higher than the 30% fault recognition rate of traditional methods. The experimental results of monitoring of the spindle production line indicated that monitoring using the WPSN encounters less interference from noise compared to that of traditional methods. Therefore, this study has successfully developed a prototype concept into a well-developed monitoring system, and the monitoring can be implemented in a spindle production line or real-time monitoring of machine tools.
Highlights
Using a monitoring system to assess the symptoms of mechanical systems prior to the occurrence of faults can effectively prevent sudden system shutdowns and any consequent impact on productivity [1].A wide variety of monitoring methods target various sensors for machine tools, such as temperature sensors, displacement sensors, speedometers, and accelerometers [2]
Because the cause of spindle faults are not analyzed through vibration signals in the time domain, data sampled at a frequency of 0.25 ms/time were transferred to a frequency domain using Fast Fourier
If a hidden monitoring system is applied to ordinary monitoring tasks, the system must be clear of interference from other systems
Summary
Using a monitoring system to assess the symptoms of mechanical systems prior to the occurrence of faults can effectively prevent sudden system shutdowns and any consequent impact on productivity [1].A wide variety of monitoring methods target various sensors for machine tools, such as temperature sensors, displacement sensors, speedometers, and accelerometers [2]. The most commonly referenced indicator of damages to the bearing of the machine tool spindle is the vibration signal. The magnitude and frequency of vibration can be used to identify the cause of system failure, such as bearing damage, impeller failure, sealing failure, and mixed failure spectrum [3,4]. With advancements in industrial technology, increasingly diverse methods are developed to analyze the faults of machine tools, including identifying the fault cause of a single component even with multiple vibration sources [4]. Apart from identifying the type of system failure, a number of studies have examined the identification of a fault cause in systems with a low SNR [9,10]. This study is based on prior research by author Lee, titled Wireless and Powerless
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