Objectives and content The project addresses the needs of manufacturers of rotating machines like gearboxes and electric motors for the purpose of final product test. The technical approach is the development of a trainable, artificial intelligence based inspection system. The system will be introduced into the assembly line for the purpose of real-time in-line quality control. Output of the system is a fault diagnosis, received by the evaluation of complex acoustic signals. Environmental conditions like aging of test bench mechanics, the influence of spare parts to the classification result, and an optimised sensor concept will be considered. End-user 1, a domestic machine manufacturer, and End-user 2, an electric motor producer, anticipate cost savings in a volume of several hundred kECU per year by automatic test procedures, and well defined fault diagnosis.
In the case of End-user 3, a manufacturer of gearboxes, an objective and reliable test is necessary with a reproductive accuracy of 3 dB compared to present 12-15 dB. The inspection system will detect and classify minimum 95% of the most frequent faults like missing parts, faulty assembled components, faulty gear mesh noise. These faults must be detected in an incipient and well-defined stage. The inspection system must work in real-time. A fault classification rate of at least 97% is envisaged. The consortium consists of three end-users, two SMEs and three research institutes universities. The end-users are first users of the inspection system under development; the end-users have complementary applications. The product ranges of the SMEs involved are complementary as well as the expertise of the research organisations BE97-4872.
Funding SchemeCSC - Cost-sharing contracts