Estimate Brush DC Motor Reliability With Confidence
When it comes to manufacturing advanced medical instruments, reliability is a critically important consideration for coreless miniature DC motors. To estimate a motor’s expected lifetime, manufacturers employ reliability tests to measure a motor’s survivability against typical stresses like torque, speed, temperatures and vibration. Because it can take months or years to test a motor’s resistance to these conditions, accelerated life testing (ALT) over an abbreviated schedule is often the most practical testing approach.
Identify Application Requirements and Devise a Testing Plan
Understand the application load points and motion profile, such as the torque-speed requirements over time with direction of rotation, the application environment and the life expectancy of the motor. Most equipment manufacturers have specific criteria regarding the life of the product they develop, which are tied to their warranty. The target life of a motor is a function of the expected life of the product.
Reliability is typically estimated by testing a few samples in a lab that replicates the actual operating conditions of the application. The testing setup should closely match customer specifications. If possible, test five to 30 samples. The more motors that you test, the more confidence you’ll have in the motor’s predicted life.
Reliability Testing and Weibull Analysis
Reliability tests are designed to determine the failure point of the motor, with the failure criteria clearly defined before starting the test. Failures can be detected by observing a sudden increase in the current being drawn, overheating, high noise, vibration or mechanical failure. Therefore, a reliability engineer needs to measure the data continuously for changes against the baseline data.
When taking samples, collect time-to-failure (TTF) data. Use the Weibull method to analyze the failure data points. Weibull distribution plots are widely used to estimate reliability because the analysis is based on a generic distribution of data that can be used for all types of failure depending on the beta value. The characteristic life is the time at which 63.2 percent of the motors will fail.
Using the Weibull plot, the life of the motor typically falls into one of three distinct regions of a classic bathtub curve. If β is less than one, the failure rate decreases with time. This distribution indicates infant or early-life failures attributable to manufacturing problems like poor soldering, high axial play or incorrect preloading. If β is equal to one, the failure rate is constant and indicates random failures. If β is greater than one, failures are caused by wear out and increase with time.
When applied and analyzed correctly, the Weibull distribution method will help you make sound reliability and longevity projections so you can select the best brushless DC motor for your application. The right manufacturer can offer motors that satisfy your reliability requirements.