Online Condition Monitoring of Traction Motor

Online Condition Monitoring of Traction Motor

Anik Kumar Samanta (Indian Institute of Technology Kharagpur, India), Arunava Naha (Indian Institute of Technology Kharagpur, India), Devasish Basu (Indian Railways, India), Aurobinda Routray (Indian Institute of Technology Kharagpur, India) and Alok Kanti Deb (Indian Institute of Technology Kharagpur, India)
DOI: 10.4018/978-1-5225-0084-1.ch020
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Abstract

Squirrel Cage Induction Motors (SCIMs) are major workhorse of Indian Railways. Continuous online condition monitoring of the SCIMs like Traction Motor (TM) are essential to prevent unnecessary stoppage time in case of a complete failure. Before a complete failure, the TMs generally develop incipient or weak faults. Weak faults have minute influence on the motor performance but eventually leads to complete failure of the motor. If these weak faults are identified at the earliest then, a scheduled maintenance can be planned which will prevent any unplanned stoppage. The signals used for SCIM fault detection are motor current, voltage, vibration, temperature, voltage induced in search coil, etc. The most popular fault detection technology is based on Motor Current Signature Analysis (MCSA). MCSA based online and onboard TM condition monitoring system can be very useful for Indian railways to reduce the cost of operation and unplanned delay by shifting from unnecessary scheduled maintenance to condition-based maintenance of TM and other auxiliary SCIMs.
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Background

Fault detection of Inductor motor requires extensive study about the different types of faults and how they are detected. To provide a better understanding, two sections have been dedicated about the faults that occurs and how they are detected. This chapter summarizes the existing techniques and gives an insight into recent technologies that are available for fault detection and diagnosis of SCIMs.

1. Induction Motor Faults and It’s Classification

Fault is defined as unpermitted deviation of at least one characteristic property of the system from the acceptable, usual, standard condition, Isermann (2006). Faults are incipient in nature, so even if there is a fault in the system, the system may operate as a normal system with subtle deviation in its states. Fault diagnosis consists of three different steps, 1. Fault detection, 2. Fault Isolation - localization or classification of the fault, and 3. Fault identification - determination of type, magnitude and cause of the fault. Failure is defined as permanent interruption of the system’s ability to perform the required functions, Isermann (2006). If faults are not detected and proper maintenance has not been taken, the faulty system leads to complete failure resulting in loss of productivity. Failure prognosis consists of early detection of incipient faults and predicting the remaining useful life before failure and is required for predictive maintenance. Figure 1 illustrates the different steps involved in fault diagnosis and failure prognosis. Faults in SCIMs can be broadly classified into stator faults, rotor faults and bearing faults. Each fault class can further be classified as given below.

  • Rotor Faults

    • o

      Broken rotor bar

    • o

      Broken end ring

    • o

      Eccentricity

      • Static Eccentricity

      • Dynamic Eccentricity

      • Mixed Eccentricity

  • Bearing Faults

    • o

      Inner raceway

    • o

      Outer raceway

    • o

      Rolling element

  • Stator Faults

    • o

      Inter turn short circuit

    • o

      Phase to phase short

    • o

      Phase to ground short

Brief description about these faults are discussed below

Figure 1.

Illustration of fault diagnosis and failure prognosis

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