Correcting Sensor Drift and Intermittency Faults With Data Fusion and Automated Learning

Many fault detection algorithms deal with fault signatures that manifest themselves as step changes. While detection of these step changes can be difficult due to noise and other complicating factors, detecting slowly developing faults is usually even more complicated. Tradeoffs between early detection and false positive avoidance are more difficult to establish. Often times, slow drift faults go completely undetected because the monitoring systems assume that they are ordinary system changes and some monitoring schemes may adapt to the changes. Where redundant sensors are used, a drifting sensor may cause the logic to latch on to the ldquobadrdquo sensor. Another problem may be intermittent sensors faults where the detection logic is too sluggish to recognize a problem before the sensor has returned to seemingly normal behavior. To address these classes of problems, we introduce here a set of algorithms that learns to avoid the bad sensor, thus indirectly recognizing the aberrant sensor. We combine advanced sensor validation techniques with learning. The sensor validation is inspired by fuzzy principles. The parameters of this algorithm are learned using competing optimization approaches. We compare the results from a particle swarm optimization approach with those obtained from genetic algorithms. Results are shown for an application in the transportation industry.