Design for diagnosability of multistation manufacturing systems based on sensor allocation optimization

System monitoring and diagnosing can be achieved through measuring sensing data. Meanwhile, diagnosability is the capability of a system in terms of diagnosing dimensional variation and root cause identification. In order to obtain enough sensing data, special attention should be taken to the sensor allocation optimization within the framework of ensuring system diagnosability, which can lower sensing cost and reduce time to diagnosis. This paper investigates both theoretically and experimentally the effect of sensor allocation optimization on the diagnosability of a multistation manufacturing system (MMS). The design for diagnosability (DFD) is formulated and implemented in the early design phase. Three indices, namely detectability, locatability, and isolability, are proposed to measure the system diagnosability. A two-step process of diagnosing variation sources is presented to specify the variation transmission between stations and variation diagnosis within a station. An optimal methodology of sensor allocation is then proposed. A typical machining process is demonstrated as an example of the analytical procedure and reference for sensor allocation in practice. Four sensing strategies are conducted for performance comparison. Results indicate that the optimal sensor allocation yields less sensing cost and shorter time to diagnosis without loss of diagnosability.