Open Cut » Maintenance & Equipment
Australian mining industry is adopting AC motors with variable speed drives in various applications, including shuttle cars, continuous miners, shearers, as well as excavating machines, such as draglines and electric rope shovels. This transition is expected to continue and possibly accelerate, necessitating development of condition monitoring tools for AC machines in mining applications.
A number of AC motor condition monitoring tools are available off-the-shelf. They are based around off-line or on-line measurement of external parameters, typically vibration or stator current, in order to detect signature harmonics indicating faults.
While this approach has proven its ability to detect faults, sensitivity of externally measured parameters is not always sufficient to capture a fault at an early stages of development. Nor do the existing methods provide accurate information on how long a motor may operate until it fails.
Additionally, most of the existing methods require significant intervals of steady state operation to be able to detect faults, but in dynamic applications, such as digging, motors almost never operate in steady state. Finally, signature harmonics indicating faults are not well pronounced under inverter control due to current regulator action and injection of switching noise.
These recognized difficulties in existing fault diagnostic tools have inspired the AC Duty Meter project. The Duty Meter approach is different from the existing techniques in that it uses the information about the motor duty (speed, load, load dynamics) to predict the development of the faults of interest.
The other big difference of the AC Duty Meter project is the advanced motor instrumentation. Along with signals measured externally to the motor, such as stator current and voltage, we use flux density signals measured inside the motor air gap by an array of Hall effect sensors. This approach has proven practical by an earlier investigation of DC motor fault mechanisms. With respect to the AC motor faults, it has brought unprecedented capabilities, which no other known condition monitoring tool has ever had, namely, it made it possible to not only detect each fault at an incipient stage but to also tell its exact location, its exact measure (severity) and to predict its further development.
Feasibility study of the AC Motor Duty Meter approach was completed within the parent C21040 project (provided with this report). This report summarizes findings of the second (and final) C24035 project, in which the algorithms of fault diagnosis and prediction are implemented in a prototype Duty Meter tool. This tool is based on National Instruments CompactRIO platform, which is suitable for industrial installations due to its robust design. National Instruments based real time data acquisition and control systems are being extensively used across mining industry, including OEMs of mining excavators.
The AC Duty Meter tool, in its present form, has been tested and validated on a laboratory scale AC motor. Its implementation with a full scale AC motor (for example, in an electric rope shovel) would only require instrumentation of the AC motor with sensors, and some changes in the Duty Meter software (such as settings associated with the motor design). No additional hardware will be required.
This report briefly describes the motor, sensors and other hardware used in the study. It touches upon the existing fault diagnostic methods, and explains how various faults can be diagnosed in a new way using the advanced sensor instrumentation. The report further explains about the Duty Meter screens and functionalities, and illustrates the diagnosis of each fault by a real life example.
Four main types of AC motor faults have been addressed by the AC Motor Duty Meter, namely, winding insulation faults; rotor bar faults; static eccentricity faults; and dynamic eccentricity faults. A large number of illustrations are included throughout the report to assist in understanding of its content. The main results are summarized in the Conclusions.