Fault detection and isolation
Fault detection and isolation is a subfield of control engineering which concerns itself with monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location. Two approaches can be distinguished: A direct pattern recognition of sensor readings that indicate a fault and an analysis of the discrepancy between the sensor readings and expected values, derived from some model. In the latter case, it is typical that a fault is said to be detected if the discrepancy or residual goes above a certain threshold. It is then the task of fault isolation to categorize the type of fault and its location in the machinery. Fault detection and isolation (FDI) techniques can be broadly classified into two categories. These include Model-based FDI and Signal processing based FDI.
In model-based FDI techniques some model of the system is used to decide about the occurrence of fault. The system model may be mathematical or knowledge based. Some of the model-based FDI techniques include  observer-based approach, parity-space approach, and parameter identification based methods.
The example shown in the figure on the right illustrates a model-based FDI technique for an aircraft elevator reactive controller through the use of a truth table and a state chart. The truth table defines how the controller reacts to detected faults, and the state chart defines how the controller switches between the different modes of operation (passive, active, standby, off, and isolated) of each actuator. For example, if a fault is detected in hydraulic system 1, then the truth table sends an event to the state chart that the left inner actuator should be turned off. One of the benefits of this model-based FDI technique is that this reactive controller can also be connected to a continuous-time model of the actuator hydraulics, allowing the study of switching transients.
Signal processing based FDI
In signal processing based FDI, some mathematical or statistical operations are performed on the measurements, or some neural network is trained using measurements to extract the information about the fault.
A good example of signal processing based FDI is Time Domain Reflectometry where a signal is sent down a cable or electrical line and the reflected signal is compared mathematically to original signal to identify faults. Spread Spectrum Time Domain Reflectometry, for instance, involves sending down a spread spectrum signal down a wire line to detect wire faults. Several clustering methods have also been proposed to identify the novel fault and segment a given signal into normal and faulty segments.
- Spread-spectrum time-domain reflectometry
- Control Theory
- Predictive maintenance
- Fault-tolerant system
- Condition monitoring
- Control reconfiguration
- System identification
- Jason R. Ghidella and Pieter J. Mosterman, "Requirements-Based Testing in Aircraft Control Design," Paper ID AIAA 2005-5886 in AIAA Modeling and Simulations Technologies Conference and Exhibit 2005, August 15-18, San Francisco, California, 2005.
- Ding, S.X., Model-based fault diagnosis techniques, Springer 2008
- Pieter J. Mosterman and Jason Ghidella, "Model Reuse for the Training of Fault Scenarios in Aerospace," in Proceedings of the AIAA Modeling and Simulation Technologies Conference, CD-ROM, paper 2004-4931, August 16 - 19, Rhode Island Convention Center, Providence, RI, 2004.
- Liu, Jie (2012). "Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection". Measurement Science and Technology 23 (5): 1–11.
- Furse,Cynthia; Smith, Paul; Lo, Chet. "Spread Spectrum Sensors for Critical Fault Location on Live Wire Networks" Structural Control and Health Monitoring June 6, 2005.
- Bahrampour,Soheil; Moshiri, Behzad; Salahshour, Karim. "Weighted and constrained possibilistic C-means clustering for online fault detection and isolation " Applied Intelligence, Vol 35, pp. 269-284, 2011 June 6th, 2005.