The complexity and sophistication of the new generation of physical systems along with the growing demand for their reliability and safety, is being met by automatic control and monitoring, and the use of functional redundancy techniques that exploit static and dynamic relations between observed variables in a system for fault detection and isolation.
We present a paradigmatic example of a feedbackcontrolled system: an electric motor with sensor and controller. Diagnosis of this system is performed based on a qualitative model that reflects deviations of parameters and behavior from a fixed reference state. The hypothesis that has been examined in this case study is that detection of behavior discrepancies does not necessarily require simulation of behavior, but can be done by checking (qualitative) states only. The qualitative models and the state-based diagnosis algorithm proved to establish a basis sufficient for fault detection and fault identification in the motor example. Some of the general preconditions for this are discussed.
The multiple fault diagnosis problem is important, since the single fault assumption can lead to incorrect or failed diagnoses when multiple faults occur. It is challenging for continuous systems, because faults can mask or compensate each other's effects, and the solution space grows exponentially with the number of possible faults. We present a qualitative approach to multiple fault isolation in dynamic systems based on analysis of fault transient behavior. Our approach uses the observed measurement deviations and their temporal orderings to generate multiple fault hypotheses. The approach has polynomial space requirements and prunes diagnoses, resulting in an efficient online fault isolation scheme.
Networked embedded systems are composed of a large number of distributed nodes that interact with the physical world via a set of sensors and actuators, have their own computational capabilities, and communicate with each other via a wired or wireless network. Diagnostic systems for such applications must address new challenges caused by the distribution of resources, the networking environment, and the tight coupling between the computational and the physical worlds. Our approach is to move from centralized, discrete or continuous techniques toward a distributed, hybrid diagnosis architecture. This paper demonstrates distributed, discrete diagnosis algorithms that leverage the topology of the physical plant to limit inter-diagnoser communication and compute diagnoses in an anytime and any information manner, making them robust to communication and processor failures. It also presents a particle filtering based estimation algorithm that addresses the challenge of the interaction between continuous and discrete dynamics in hybrid systems. The distributed qualitative diagnosis and hybrid estimation techniques are demonstrated using a rocket propulsion system.
Integrated Systems Health Management (ISHM) provides the ability to maintain system health and performance over the life of safety-critical systems. This paper discusses a model-based approach to diagnosis and prognosis of safety-critical systems that combines fault detection, isolation and identification, faultadaptive control, and prognosis into a common framework. At the core of this framework are a set of component oriented physical system models. By incorporating physics of failure models into component models the dynamic behavior of a failing or degrading system can be derived by simulation. Current state information predicts future behavior and performance of the system to guide decision making on system operation and maintenance.