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.
Pi maer This paper describes three application domains for integrating planning, scheduling and execution at NASA Johnson Space Center. The three domains are: advanced life support systems; traded control of robotic manipulators and free flying space robots. The challenges of each domain will be given and initial progress will be described. For each domain we have applied the same layered control architecture, called 3T, which will also be described in this paper.
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.
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.
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.