An integrated framework for distributed diagnosis of process and sensor faults

Complex engineering systems require efficient online fault diagnosis methodologies to improve safety and reduce maintenance costs. In complex systems, faults may occur in the process itself but also in the sensors monitoring the system, which makes the fault diagnosis task difficult, because the signals from which diagnostic reasoning takes place may be corrupted by faulty sensors. As such, many diagnosis solutions focus on either process or sensor faults, but not both. When considering both types of faults, additional diagnostic information is needed because of the additional ambiguity introduced by potentially faulted sensors. As such, traditional centralized diagnosis approaches, which already do not scale well, scale even worse. To address these issues, this paper presents a distributed diagnosis framework for physical systems applied to diagnosis of both sensor and process faults. Using a structural model decomposition method, we develop a distributed diagnoser design algorithm to build local fault diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, determining the minimal number of residuals required to have the maximum possible diagnosability in the system. We evaluate the design approach on a diagnostic benchmark system that is functionally representative of a spacecraft electrical power distribution system. Results demonstrate that the proposed distributed approach scales significantly better than a centralized approach.