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 as deviations compared to a normal sound pro-file. This is the case in robot assembly, the selected application domain for the system. Manual diagnosis based on sound requires extensive experience, and the experience is often acquired through costly mistakes and reduced production efficiency or quality loss caused by missed faults. The acquired experience is also difficult to preserve and transfer, and often lost if personnel leave the task of testing and fault diagnosis. We propose a Case-Based Reasoning approach to collect and preserve experience. The solution enables fast experience transfer and leads to less experienced personnel required to make more reliable and informed testing. Sounds from normal and faulty equipment are recorded and stored in a case library together with a diagnosis. Addition of new validated sound profiles continuously improves the system’s performance. The system can preserve and transfer experience between technicians, reducing overall fault identification time and increases quality by reduced number of missed faults. The original sound recordings are stored in form of the extracted features to-gether with other experience, e.g. instructions, additional tests, advice, user feedback etc.