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are as follows (Basseville and Nikiforov 1993; Chen and Patton 1999):
1 1) probabilistic reasoning;
2 2) possibilistic reasoning with fuzzy logic;
3 3) reasoning with artificial neural networks.
This very short consideration shows that many different methods have been developed over the last 30 years. It is also clear that many combinations of them are possible.
On the basis of different contributions during the last 30 years, it can be stated that parameter estimation and observer-based methods are the most frequently applied techniques for fault detection, especially for the detection of sensor and process faults. Nevertheless, the importance of neural network-based and combined methods for fault detection is steadily growing. In most applications, fault detection is supported by simple threshold logic or hypothesis testing. Fault isolation is often carried out using classification methods. For this task, neural networks are being more and more widely used.
The number of applications using nonlinear models is growing, while the trend of using linearized models is diminishing. It seems that analytical redundancy-based methods have their best application areas in mechanical systems where the models of the processes are relatively precise. Most nonlinear processes under investigation belong to the group of thermal and fluid dynamic processes. The field of applications to chemical processes has few developments, but the number of applications is growing. The favorite linear process under investigation is the DC motor. In general, the trend is changing from applications to safety-related processes with many measurements, as in nuclear reactors or aerospace systems, to applications in common technical processes with only a few sensors. For diagnosis, classification and rule-based reasoning methods are the most important, and the use of neural network classification as well as fuzzy logic-based reasoning is growing.
I.9. FDI application report
Because of the many publications and increasing number of applications (IFAC Congress and IFAC Symposia SAFEPROCESS) between 1991 and 2018, it is of interest to show some trends (Patton et al. 1989; Basseville and Nikiforov 1993; Gertler 1998; Chen and Patton 1999; Frank et al. 2000). Therefore, a literature study is briefly presented as follows. Contributions taking into account the applications reported in Table I.1 were considered. The type of faults considered is distinguished according to Table I.2. Among all contributions, the fault detection methods were classified as in Table I.3. The change detection and fault classification methods are indicated in Table I.4. The reasoning strategies for fault diagnosis are reported in Table I.5. The contributions considered are summarized in Table I.6. The evaluation has been limited to the fault detection and diagnosis (FDD) of laboratory, pilot and industrial processes.
Table I.1. FDI applications and number of contributions
| Application | Number of contributions |
|---|---|
| Simulation of real processes | 105 |
| Large-scale pilot processes | 94 |
| Small-scale laboratory processes | 68 |
| Full-scale industrial processes | 98 |
Table I.2. Fault type and number of contributions
| Fault type | Number of contributions |
|---|---|
| Sensor faults | 129 |
| Actuator faults | 111 |
| Process faults | 123 |
| Control loop or controller faults | 48 |
Table I.3. FDI methods and number of contributions
| Method type | Number of contributions |
|---|---|
| Observer | 123 |
| Parity space | 74 |
| Parameter estimation | 101 |
| Frequency spectral analysis | 57 |
| Neural networks | 79 |
Table I.4. Residual evaluation methods and number of contributions
| Evaluation method | Number of contributions |
|---|---|
| Neural networks | 89 |
| Fuzzy logic | 65 |
| Bayes classification | 54 |
| Hypothesis testing | 48 |
Table I.5. Reasoning strategies and number of contributions
| Reasoning strategy | Number of contributions |
|---|---|
| Rule based | 40 |
| Sign directed graph | 33 |
| Fault symptom tree | 32 |
| Fuzzy logic | 66 |
Table I.6. Applications of model-based fault detection