Article Details

An Analysis on Motor Current and Vibration Signatures and Its Uses in Fault Detection of Induction Motor | Original Article

Jasani Chiragkumar Natvarlal*, Yashpal Singh, in Journal of Advances in Science and Technology | Science & Technology


Motor electrical current signature analysis (MCSA) is sensing an electrical signal con¬taining current components that are direct by-product of unique rotating flux components. Anomalies in operation of the motor modify harmonic content of motor supply current. This paper presents brief introductory review of the method including fundamentals, fault detection techniques and current signatures of various faults. Induction motors are used worldwide as the “workhorse” in industrial applications. Although, these electromechanical devices are highly reliable, susceptible to many types of faults. Condition monitoring and fault diagnosis of induction motors are of great importance in production lines. It can significantly reduce the cost of maintenance and the risk of unexpected failures by allowing the early detection of potentially catastrophic faults. In this paper I have used both vibration and motor current signature analysis to detect the fault. The various fault discussed in this paper are- Mechanical fault such as bearing damage and Electrical Fault such as unbalanced voltage supply, single phasing. Condition monitoring, signal processing and data analysis are the key parts of the Induction Motor fault detection scheme. The Motor Current Signature Analysis (MCSA) is considered the most popular fault detection method now a day because it can easily detect the common machine fault such as turn to turn short ckt, cracked /broken rotor bars, bearing deterioration etc. The present paper discusses the fundamentals of Motor Current Signature Analysis (MCSA) plus condition monitoring of the induction motor using MCSA. In addition, this paper presents four case studies of induction motor fault diagnosis. The results show that Motor current signature analysis (MCSA) can effectively detect abnormal operating conditions in induction motor applications.