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The purpose of this work is Fault Detection and Diagnosis (FDD) on a Knock Sensor because some of the modern petrol engines operate on the efficient four-stroke cycle, where each cylinder of the engine contains an intake and exhaust poppet valve that is operated at the appropriate time. The ECM (Engine Control Module) uses the Knock Sensor signal to control timing. The Knock Sensor detects engine knock and sends voltage signal to the ECM. These signals can be sufficient to detect abnormal combustion, like ‘spark knock’ and ‘surface ignition’. Engine knock occurs within a specified range. The Knock Sensor, located in the engine block, cylinder head, or intake manifold is tuned to detect that frequency, which motivates the use of signal models for detection. But this sensor is a wide-band accelerometer of the piezoelectric type too. Analogy with a general seismic mass system is possible since it is a general damped second order vibrating system which is forced into oscillatory motion.

In this work, the problem of fault detection, isolation, and reconfiguration (FDIR) for Networked-Control Systems (NCS) of aerospace vehicles is discussed. The concept of fault-tolerance is introduced from a generic structure, and a review on quantitative and qualitative methods (state estimation, parameter estimation, parity space, statistic testing, neural networks, etc.) for FDIR is then performed. Afterwards, the use of networks as loop-closing elements is introduced, followed by a discussion on advantages (flexibility, energy demand, etc.) and challenges (networks effects on performance, closed-loop fault-effects on safety, etc.) represented thereby. Finally, examples of applications on aerospace vehicles illustrate the importance of the discussion herein exposed.

Several papers presents fault detection and isolation techniques for fault in only one sensor; in this paper we will present a technique for multiples faults detection and isolation in sensors of dynamic systems. Multiples faults have less probability to occur but it is not null. So in critical applications the system needs to be operational even in this situation. In this paper we will present a design for a Multiples Faults Detection and Isolation (MFDI) system, an example to illustrate this technique and its respective results.

Eigenstructure techniques allow to detect and isolate faulty components in a dynamic process, such as sensor biases, actuator malfunctions, changes in dynamic parameters due to leaks and deterioration. Fault detection is the first step to achieve fault tolerance, but for this the redundancy has to be included in the system. This redundancy can be either by hardware or by software. In situations in which it is not possible to use hardware redundancy only the software redundancy can be used. Therefore using eigenstructure techniques, for the fault detection and isolation, the tests can be done through the angle between the residue vector direction and the fault direction vector. By this way, we can reduce false alarm and the alarm loss rates due to the noise and changes in system parameters.

Currently, aerospace and automotive industries are developing complexand/or highly integrated systems, whose services require greater confidence to meet a set of specifications that are increasingly demanding, such as successfully operating a communications satellite, a commercial airplane, an automatic automobile, and so on. To meet these requirements and expectations, there is a growing need for fault treatment, up to predict faults and monitor the health of the components, equipment, subsystems or systems used. In the last decades, the approaches of 1) Fault Prevention, 2) Fault Detection/Tolerance and 3) Fault Detection/Correction have been widely studied and explored.