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

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 u...

• Type: Conference
• Year: 2004
• Where: EWCBR
• Authors: Erik Olsson, Peter J. Funk, Marcus Bengtsson
• Keywords: Artificial Intelligence, Case Library, EWCBR 2004, Experience, Manufacturing Rigorous Testing