Analysis Of Fault Diagnosis Of Dc Motors By Power Consumption Pattern Recognition

• Main Authors: Majdi H.S., Shijer S.S., Hanfesh A.O., Habeeb L.J., Sabry A.H.
• Other Authors: 55898722600
• Format: Article
• Published: Technology Center 2023
• Institution: Universiti Tenaga Nasional

Early detection of faults in DC motors extends their life and lowers their power usage. There are a vari-ety of traditional and soft computing techniques for detecting faults in DC motors. Many diagnostic tech-niques have been developed in the past to detect such fault-related patterns. These methods for detecting the aforementioned potential failures of motors can be utilized in a variety of scientific and technological domains. Motor Power Pattern Analysis (MPPA) is a technology that analyzes the current and voltage pro-vided to an electric motor using particular patterns and protocols to assess the operational status of the motors without disrupting production. Engineers and researchers, particularly in industries, face a diffi-cult challenge in monitoring spinning types of equip-ment. In this work, we are going to explain how to use the motor power pattern/signature analysis (MPPA) of a power signal driving a servo to find mechanical defects in a gear train. A hardware setup is used to simplify the demonstration of obtaining spectral met-rics from the power consumption signals. A DC motor, a set of metal or nylon drive gears, and a control cir-cuit are employed. The speed control circuit was elim-inated to allow direct monitoring of the DC motor�s current profiles. Infrared (IR) photo-interrupters with a 35 mm diameter, eight-holed, standard servo wheel were employed to gather the tachometer signal at the servo�s output. The mean value of the measurements was 318 V for the healthy profile, while it was 330 V for the faulty gears power data. The proposed power consumption profile analysis approach succeeds to recognize the mechanical faults in the gear-box of a DC servomotor via examining the mean level of the power consumption pattern as well as the extraction of the Power Spectral Density (PSD) through comparing faulty and healthy profiles � 2021, Authors. This is an open access article under the Creative Commons CC BY license