An expert system to diagnose failures in industrial robots.

1

Abnormal behaviors in industrial robots are analyzed. Assuming that the robot is well tested during design phase, abnormalities during routine robot operations are traced to two kinds of erroneous situations. 1) Operational errors resulting from encountering unexpected environment such as missing part, misorientation in part/tool etc., 2) A fatal hardware failure in the electronic circuitry. An expert system is designed that takes control of the robot during abnormal situations, determines whether abnormality is due to a recoverable fault or fatal hardware failure. The expert system will either activate built-in error recovery routines or goes through a hardware diagnostic phase respectively. Diagnosis is based on the diagnostic information provided by the event trace at the time of abnormal behaviour and some move the arm is forced to make for more diagnostic information.

2

In a robot system, any situation where the performed task is a deviation (отклонение) from the programmed task is called an abnormality. Robot end-effector failing to move along preplanned trajectories, end-effector unable to close/open grip (захват), failures in the synchronization of arm movements with respect to movements of parts, failures in servo-feedback mechanisms are some such abnormalities. It is possible to recover the robot from certain types of abnormalities, such as missing parts, misorientation of part/tool etc. These recoverable abnormalities are named (operational) errors. On the other hand, failures in sensory systems, faults in analog/digital units, motor failures etc. are named hardware faults. Usually, it is not possible to completely recover the robot arm when (hardware) faults are present. However, if the motors and the servo systems controlling the motors are functioning properly, then it is possible to force the arm to make some preprogrammed moves exclusively for diagnostic use.

An expert system is proposed exclusively for fault diagnosis and error recovery in industrial robots. The main functions of such an expert systems are: 1) to monitor robot performance; 2) identify abnormal behaviors beyond tolerance limits; 3) identify the type of failures, i.e. operational errors or hardware faults; 4) activate error recovery software routines, if the abnormality is due to operational errors and 5) stop normal robot operation and activate fault diagnosis phase, if the abnormality is due to hardware failures.

3

The expert system works in conjunction with the robot controller unit. During normal operation, the controller will execute the exact program continuously monitoring the new values of the variables of motion of the end-effect. These new values are fed back to the controller by the sensory system, and it is very important for the controller to make corrections to parameters such as linear velocity, angular velocity etc. We assume that the controller stores the traces of values corresponding to the most recent end-effector moves. This trace is called event trace.

Abnormalities in end-effector movements are first detected by the controller when the event trace observed does not confirm with the expected trace values. When the differences between these two sets of values are beyond tolerance limits, then the controller stops the present move and gives the control to expert system. The expert system must first decide whether the abnormality is due to an operational error or a hardware fault.

When controller activates the expert system due to an abnormality, the expert system goes through the above steps to determine the abnormality cause. In case of operational errors, the expert system activates error service routines, retracts the arm from previous position and initiate next moves. If the abnormality is due to a hardware failure, it prints a message about the presence of hardware failure and goes into hardware cases when an abnormality is caused by an operational error and a hardware failure, it first goes through an error recovery to the extent permitted by the hardware failure and then goes through the hardware diagnosis phase.

4

Hardware failures are caused by open circuits in 1/0 ports, sensors burning due to overload or age, faults in electronic units such as ADS, power amplifiers, differential/integrating analog circuitry, failure in servo systems, failures in processor units, digital circuitry, etc. The internal electronic net of a robot system will have both analog and digital circuitry working in conjunction with sensory units, motor control and the main controller. This makes modeling of this network very tedious (утомительный) for fault diagnosis. Traditional expert system uses a combination of the diagnostic information gathered by the event trace when abnormalities occurred, the diagnostic information obtained by forcing the arm along preplanned moves and a set of signal values obtained by simulation used as diagnostic test patterns, for the hardware fault diagnosis.

5

The expert system conducts the diagnosis experiments in steps, each step refining the diagnostic information obtained in the previous step. For industrial robot systems, the total number of distinct hardware faults will be enormous. It is not necessary to locate the error to the level of a wire. Diagnosis will be limited to identifying the faulty unit such as sensory unit etc. The expert system will hold the following knowledge about the internal circuitry:

1. The set of all faults are grouped into classes, where the faults in a class will make feed-back values from one sensory unit erroneous. These classes are not disjoint as one fault may make readings from more than one sensory units erroneous.

2. For each sensory unit, a set of dummy arm (макет руки) moves are designed to check the correct operation of the sensory unit.

3. A set of diagnostic test sequences are designed to check each distinguishable segment in each path for correct operation.

This can be done either by simulation techniques or fault diagnosis algorithms.

6

The diagnosis experiment consists of the following steps.

1) When a hardware failure is detected, it first checks whether motor are operating correctly. If there is an erroneous motor, it is replaced. If this is the only error then exit.

2) The event trace before the detection of the abnormality is inspected by the expert system. All selections of hardware (including sensory units, digital and analog circuitry etc.), that could possibly cause the erroneous event trace, are identified.

3) The expert system takes the robot arm through the predesigned sets of movements to further improvement of the diagnostic information obtained if Step 2. All arm movements that show no error in their track data will identify the fault-free sections of hardware. This will greatly restrict the area of search for the hardware fault.

4) At the end of Step 3, fault location can be determined to within a few paths of signal flow. To further localize the faulty unit the system will use test sequences and will identify the fault to a unit/package level.

7

Conclusion. An expert system is presented that works in conjunction with the robot controller. In case of abnormal robot behaviour, the expert system will take control from the controller, determines whether the abnormality is a recoverable behaviour error or a fatal hardware fault. If it is of the first type, it will activate the built-in error recovery routines. If it is a hardware failure, then it will go through hardware diagnosis phase. The idea is to make the robot self-sufficient in diagnosis. Human interference is minimized and is required only when the identified faulty unit is to be replaced. Failures in motors are not common, but when there is a motor failure, the expert system cannot force the arm to make dummy moves for diagnosis. The only way to overcome this problem is to first repair the faulty motor and then going through the diagnostic phase.

 

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