Fault Diagnosis of Industrial Robots Using Acoustic Signals and Case-Based Reasoning

Abstract

In industrial manufacturing rigorous testing is used to ensure that the delivered products meet their specifications. Mechanical maladjustment or faults often show their presence through abnormal acoustic signals. This is the same case in robot assembly – the application domain addressed in this paper. Manual diagnosis based on sound requires extensive experience, and usually such experience is acquired at the cost of reduced production efficiency or degraded product quality due to mistakes in judgments. The acquired experience is also difficult to preserve and transfer and it often gets lost if the corresponding personnel leave the task of testing. We propose herein a Case-Based Reasoning approach to collect, preserve and reuse the available experience for robot diagnosis. This solution enables fast experience transfer and more reliable and informed testing. Sounds from normal and faulty robots are recorded and stored in a case library together with their diagnosis results. Given an unclassified sound signal, the relevant cases are retrieved from the case library as reference for deciding the fault class of the new case. Adding new classified sound profiles to the case library improves the system’s performance. So far the developed system has been applied to the testing environment for industrial robots. The preliminary results demonstrate that our system is valuable in this application scenario in that it can preserve and transfer the related experience among technicians and shortens the overall testing time.