Abstract

This paper proposes a novel method that uses stator current signals to detect motor faults under operational speed and load torque conditions. Previous studies on motor current signature analysis (MCSA) have been devoted to developing methods to detect faults in non-stationary conditions; however, they have limitations. Conventional methods require much domain knowledge or parameter selection for signal decomposition, and are applicable under limited variable conditions. Thus, this paper proposes a new feature, drive-tolerant current residual variance (DTCRV), for fault detection. This new approach requires no domain knowledge and is applicable under varying speed and load torque conditions. In the proposed method, first, the envelope of the current signal is calculated to extract its modulation. Second, the drive-related signal, which greatly varies based on speed and load torque conditions, is extracted from the enveloped current signal. Third, the drive-tolerant current residual (DTCR) is calculated; the DTCR is defined as the subtraction of the drive-related signal from the enveloped current signal. Finally, the new health feature is calculated as the variance of the DTCR. To demonstrate the proposed method, experimental studies were conducted under several operating conditions (i.e., different speed profiles and load torque levels) with two fault modes: 1) a stator inter-turn short and 2) misalignment. Results confirm the ability of DTCRV to promptly and accurately detect faults in a variety of conditions; in contrast, conventional methods are greatly affected by the operating conditions.

Highlights

  • Industrial motors are widely used in lots of manufacturing processes and permanent magnet synchronous motors (PMSMs), which are one type of industrial motors, are usually integrated into many types industrial equipment that perform precise control, such as industrial robots, cooperative robots, and CNC machines [1]–[3]

  • The work outlined in this paper offers four primary contributions: 1) The proposed drive-tolerant current residual variance (DTCRV) feature can detect faults under operational speed profiles and various load torque conditions

  • 1) DTCRV was able to detect a fault without being affected by the driving condition, while the conventional methods were dominated by the driving conditions

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Summary

INTRODUCTION

Industrial motors are widely used in lots of manufacturing processes and permanent magnet synchronous motors (PMSMs), which are one type of industrial motors, are usually integrated into many types industrial equipment that perform precise control, such as industrial robots, cooperative robots, and CNC machines [1]–[3]. Considering that industrial machines operate in various load and speed conditions, the development of robust fault detection methods that can be applied in non-stationary conditions with minimal expertise is still required. This paper proposes a novel method to detect motor faults under variable speed and load torque conditions. The work outlined in this paper offers four primary contributions: 1) The proposed DTCRV feature can detect faults under operational speed profiles and various load torque conditions. This is meaningful because most real-world data are acquired under unconstrained driving conditions.

BACKGROUNDS
THE RELATION OF THE STATOR CURRENT AND
STATOR CURRENT SIGNATURE DUE TO FAULTS
EXPERIMENTAL STUDIES
EXPERIMENT 1
EXPERIMENT 2
Findings
CONCLUSION
Full Text
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