Waveform identification with personal computer

Yun Chi Yeh, Hong Jhih Lin, Wen June Wang, Che Wun Chiou

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This study proposes a simple, fast and reliable method, termed "Motor's Current Waveform Identification Method" (MCWIM), to analyze current waveforms for recognizing whether the motor is good or not. If the motor is defect, kind of defect is also determined. The proposed MCWIM consists of four procedures: (1) The automatic power adding device (APAD) supplies power to the motor, and then the device can send out signals to waveform detect circuit (WDC) simultaneously. (2) The current waveform which is amplified by a gain programmable amplifier (GPA) circuit with appropriate amplitude is inputted to the WDC. Such analog signals in WDC are transformed to digital data through the ADC circuit and then are applied to the personal computer (PC). (3) The PC analyzes and searches distinctive features of received data. Therefore, whether the motor is good or not can be decided by distinctive features. (4) The PC will pass its test result to the motor classification circuit (MCC). The proposed MCWIM has been coded by assembly language on a personal computer. Experimental results show that the error rates are 7.377% and 0.294% for type A and B, respectively. The right rates are 92.622% and 99.705% for type C and D, respectively. Type A (B) represents wrong judgment on defect (good) motor to be determined as good (defect). Type C (D) represents right judgment on defect (good) motor to be determined as defect (good).

Original languageEnglish
Pages (from-to)789-794
Number of pages6
JournalAdvanced Science Letters
StatePublished - 2012


  • ADC
  • Motor
  • Waveform identification


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