Like plastic cards, in many regards the electric motor fits the phrase “buy now, pay later.” The initial cost of the motor is a negligible fraction of its total cost of ownership. Minimize the cost of ownership not only by using only highly efficient motors, but by paying attention to the costs associated with unreliable motors driving your process or equipment.
Motors are central to the operation of many industrial processes and motor failure almost inevitably leads to equipment failure. One reason motor reliability is important in the industrial sector is the huge costs associated with lost production and downtime. Motor reliability is crucial to equipment operation and smooth-running processes, and an unreliable motor can be costly.
Why motors fail
Over many years, we’ve learned which motor components fail most often. Most issues related to induction motors, the industrial workhorse, apply to other motor types as well. It’s well known that bearing failure is the predominant motor failure mode, followed closely by winding failure. A survey by the IEEE subcommittee on power system reliability on large industrial and commercial motors and an independent study by EPRI confirm that bearing failure is the leading reason for motor failure.
Together, bearing and winding failures constitute at least 60% of all motor failures. The survey on large motors also reported that 37% of bearing failures and 33% of winding failures occur during normal plant operation.
Plant and maintenance personnel don’t get excited when a motor fails in normal operation and they’d rather be working the other shift when it happens. However, a system to detect incipient faults and warn of imminent failure on every motor would be too costly, so such systems are applied only to critical motors. Therefore, some failure rate is inevitable in normal operations.
But, how many motor failures can the plant tolerate? Whether the issue is large motors operating in plants or small motors operating in an OEM’s main product line, the realities of the global market provide common ground for the way users approach motor issues. Many industries, manufacturers and end users are under intense pressure to reduce the cost of production, the cost of products and cost of warranty claims just to stay competitive.
In the quest to reduce costs, every dollar counts and the cost of the drive motor is a primary item that comes up frequently. For large facilities, OEMs or anyone who buys motors by the truckload, a dollar of unit cost saved is a big deal. But when motor cost is on the radar screen, it’s important to evaluate the reliability of other manufacturer’s offerings against the motor that’s been giving you a headache. The number of candidates depends on the confidence level you require and how much you want to spend on the evaluation.
If your motors have been reliable in the past, you’re faced with tough questions:
- How do we move to a new, unfamiliar motor?
- How can we make sure it will work well here?
- How will this change affect our life-cycle cost?
These are but a few questions that arise, and getting answers isn’t easy because the answers are inextricably linked to motor reliability.
Define motor reliability
Reliability is the probability that a system will perform satisfactorily for at least a given time period when used under stated conditions. This probability, expressed as a function of time, is called the reliability function, R(t). For years, reliability engineers have used mean-time-to-failure (MTTF) to measure reliability for non-repairable equipment, and mean-time-between-failures (MTBF) for repairable equipment. Assuming a constant failure rate (lambda) and an exponential distribution, the mean time between failure and the reliability function (R(t)) are expressed as:
MTBF = Total operating time/Number of failures
Properly applied motors usually exhibit high reliability and also are generally repairable. However, in most cases, it makes more sense to replace small motors than to repair them. The events that are considered as total or partial failure will vary by equipment type and by plant. While total failure is easily understood, partial failure is difficult to generalize and the user must define it depending on the level of trouble-free operation required.
From the definition of MTBF, it’s clear that quantifying reliability requires accurate and dependable data. Plant personnel can collect dependable historical failure data during operation and use it to evaluate reliability, but this is much more involved than it seems because plant personnel must take note of the reasons for failure, which could be time consuming or impossible. Also, the operating environment must be recorded because different motors in different environments are exposed to different conditions.
Another way to get failure data is through model predictions, which analyze equipment and system components to determine the failure rate of the entire assembly by using complex mathematical formulations.
The empirical method for getting failure data is through physical testing. This approach is more suitable for small motors.
You might need a number expressing motor reliability for some critical process. Obtain this reliability number by a carefully designed testing of a number of identical units. There are two main ways this can be achieved.
The first involves measuring the time to failure for a sample population of motors, fitting the data to an appropriate distribution, and analyzing the results, which reveals the probability of the motors having a particular lifetime. The sample of motors needs to be tested until each fails, and no one can predict how long that might take. These tests monopolize special laboratory facilities and equipment for the duration of the test program.