The monitoring system for machinery and equipment in the project is usually composed of a single sensor due to cost constraints, that is, each sensor has its own dedicated monitoring object. This single measurement lacks complementarity and fault tolerance. The monitoring systems of precision robots and complex automatic devices are often composed of multiple sensors, and use multi-level and multi-angle measurement. For example, judging the distance between the robot and the workpiece is to fuse the measurement values ​​of multiple sensors such as overhead vision, ultrasound and other sensors to fuse the multi-sensor data signals to remove abnormal and unreasonable data and make the judgment more accurate.
According to the composition and function of the monitoring object, the motor type test is based on the monitoring of similar parameters as a grouping principle. The motor type test divides multiple independent sensors at different positions in a subsystem (or a function) into a group . In this way, each dedicated sensor in each group is like a multi-sensor monitoring system for the group. By comprehensively processing its signal, a more reasonable judgment can be made on the state of the part monitored by the group of sensors. To some extent, it is similar to fuzzy diagnosis of mechanical faults.
According to the multi-sensor signal fusion theory, the relevant sensor signal should change correspondingly under certain conditions, otherwise it will be regarded as sensor failure or failure. To determine the degree of change of each sensor signal in a specified state, the conditional probability method is generally used to calculate, and the method of excluding all strain signals to find the unchanged signal is obviously more difficult to obtain. If you consider the problem from another angle, and analyze the state of the sensor from the abnormal signal, you can greatly simplify the calculation.
In a group of sensors, there is a certain relationship between the monitoring signals. In the equipment failure state, each signal has a corresponding change. When an abnormality is detected when the signal of a certain sensor exceeds a certain limit, it is checked whether the signals of other sensors in the same group have changed at the same time. At this time, further judge the difference between each sensor in the group and the system status value. If the sensor signal cannot change with the system state value, it means that the sensor cannot reflect the system state. When the absolute value of this gap is less than a certain threshold, the sensor is normal, otherwise the sensor is abnormal.
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