Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design
Abstract
:1. Introduction
- It is based on the classical approach to training neural networks and can be realized with state-of-the-art approaches available in the literature;
- Having, an initial structure and parameters of a neural network a novel Modified Nonlinear Quasi-OBE Algorithm (MNQOA) with optimal input sequence is used to gradually decrease its uncertainty. After this process, an adaptive threshold is obtained for the resulting neural network.
- The neural network obtained in Stage 2 along with an associated adaptive threshold are used for the on-line RFD.
2. Related Works: Active and Passive Approaches to Fault Detection
- fault detection:
- it makes it possible to undertake a diagnostic decision concerning the fault. In other words, it provides a binary decision concerning the fault, e.g., a pump is faulty;
- fault isolation:
- it enables to determine the location of the fault, e.g., an induction motor driving the pump is faulty;
- fault estimation:
- it allows to determine the size of the fault as well as its time varying nature, e.g., the induction motor driving the pump is faulty and it losts 20% of its performance.
- Active fault detection:
- aims at eliminating modeling uncertainty;
- Passive fault detection:
- focuses on providing a adaptive threshold forming an enclosure associated with modeling uncertainty.
3. Model-Based RFD
4. Modified Non-Linear Quasi OBE Algorithm
- Initialization:
- Sequential iteration:
- Calculate:
5. Enhancing Performance of Neural Networks with SDED
- Step 1:
- Set , determine an initial parameter by exploiting the collected input-output data measurement set of the diagnosed system in the nominal state, set where denotes sufficiently large constant. Select , the number of gradual improvement iterations.
- Step 2:
- Obtain by D-optimal input value, and then fed it into the modeled system to get .
- Step 3:
- Update the parameter estimate. If then STOP the algorithm, else set and go to Step 2.
6. Adaptive Thresholds for the RFD
7. An Illustrative Example of the RFD
7.1. Experimental Study with the Three-Screw Spindle Oil Pump
- —the differential pressure between inlet and outlet of the pump,
- —the motor speed,
- —the torque of the pump.
7.2. Experimental Study with the Two-Tank System
8. Conclusions
Funding
Conflicts of Interest
References
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Three-Screw Spindle Oil Pump | Two-Tank System | |
---|---|---|
EKF | 1.9880 | 0.4191 |
Classical MLP | 1.7025 | 0.3256 |
MLP with SDED | 0.5108 | 0.0788 |
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Mrugalska, B. Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design. Appl. Sci. 2018, 8, 1290. https://doi.org/10.3390/app8081290
Mrugalska B. Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design. Applied Sciences. 2018; 8(8):1290. https://doi.org/10.3390/app8081290
Chicago/Turabian StyleMrugalska, Beata. 2018. "Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design" Applied Sciences 8, no. 8: 1290. https://doi.org/10.3390/app8081290