Fault detection and isolation
[3][4] 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.
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.
To identify the most probable faults leading to failure, many methods are used for data collection, including vibration monitoring, thermal imaging, oil particle analysis, etc.
Even if the machine is running in the steady state, the rotational speed will vary around a steady-state mean value, and this variation depends on load and other factors.
To be more specific, if the RPM of a machine is increasing or decreasing during its startup or shutdown period, its bandwidth in the FFT spectrum will become much wider than it would be simply for the steady state.
This is the subject of maintenance, repair and operations; the different strategies include: In fault detection and diagnosis, mathematical classification models which in fact belong to supervised learning methods, are trained on the training set of a labeled dataset to accurately identify the redundancies, faults and anomalous samples.
[16] In many industrial cases, the effectiveness of kNN has been compared with other methods, specially with more complex classification models such as Support Vector Machines (SVMs), which is widely used in this field.
Thanks to their appropriate nonlinear mapping using kernel methods, SVMs have an impressive performance in generalization, even with small training data.
[20] Artificial Neural Networks (ANNs) are among the most mature and widely used mathematical classification algorithms in fault detection and diagnosis.
ANNs are well-known for their efficient self-learning capabilities of the complex relations (which generally exist inherently in fault detection and diagnosis problems) and are easy to operate.
Hence, often, some regularization terms and prior knowledge are added to the ANN model to avoid over-fitting and achieve higher performance.
Moreover, properly determining the size of the hidden layer needs an exhaustive parameter tuning, to avoid poor approximation and generalization capabilities.
[20] In general, different SVMs and ANNs models (i.e. Back-Propagation Neural Networks and Multi-Layer Perceptron) have shown successful performances in the fault detection and diagnosis in industries such as gearbox,[22] machinery parts (i.e. mechanical bearings[23]), compressors,[24] wind and gas turbines[25][26] and steel plates.
By using Convolutional neural networks, the continuous wavelet transform scalogram can be directly classified to normal and faulty classes.
[18] Fault Recovery in FDIR is the action taken after a failure has been detected and isolated to return the system to a stable state.
