Abstract
In this paper, a new concept of “similar fault” is introduced to the field of fault isolation (FI) of discrete-time nonlinear uncertain systems, which defines a new and important class of faults that have small mutual differences in fault magnitude and fault-induced system trajectories. Effective isolation of such similar faults is rather challenging as their small mutual differences could be easily concealed by other system uncertainties (e.g., modeling uncertainty/disturbances). To this end, a novel similar fault isolation (sFI) scheme is proposed based on an adaptive threshold mechanism. Specifically, an adaptive dynamics learning approach based on the deterministic learning theory is first introduced to locally accurately learn/identify the uncertain system dynamics under each faulty mode using radial basis function neural networks (RBF NNs). Based on this, a bank of sFI estimators are then developed using a novel mechanism of absolute measurement of fault dynamics differences. The resulting residual signals can be used to effectively capture the small mutual differences of similar faults and distinguish them from other system uncertainties. Finally, an adaptive threshold is designed for real-time sFI decision making. One important feature of the proposed sFI scheme is that: it is capable of not only isolating similar faults that belong to a pre-defined fault set (used in the training/learning process), but also identifying new faults that do not match any pre-defined faults. Rigorous analysis on isolatability conditions and isolation time is conducted to characterize the performance of the proposed sFI scheme. Simulation results on a practical application example of a single-link flexible joint robot arm are used to show the effectiveness and advantages of the proposed scheme over existing approaches.
Highlights
Fault isolation (FI) is an important and challenging problem that has been extensively investigated in the systems and control community [1]–[3]
2) An adaptive threshold mechanism based similar fault isolation (sFI) approach is proposed by developing a bank of sFI estimators through a novel mechanism of absolute measurement of fault dynamics differences, which are capable of capturing the small fault differences and distinguishing them from system uncertainties, and are able to overcome the aforementioned sign issue suffered by existing FI approaches
By utilizing the constant NN model WisT S(x, u) (i = 1, · · ·, n, s = 1, · · ·, N ) obtained from the learning phase, we propose to construct a bank of sFI estimators embedded with a novel mechanism of absolute measurement of faulty dynamics differences as follows: esi (k) = biesi (k − 1) + |fi(x(k − 1), u(k − 1)) +WisT S(x(k − 1), u(k − 1)) − xi(k)|, (12)
Summary
Fault isolation (FI) is an important and challenging problem that has been extensively investigated in the systems and control community [1]–[3]. In the case when the fault differences have frequently-changing signs, their accumulated effect could be offset and approach zero, leading to possible FI misjudgment In our scheme, this issue is addressed based on a novel mechanism of absolute measurement of. The main contributions of this research work are summarized as follows: 1) The sFI problem of discrete-time nonlinear uncertain systems is addressed, where the considered similar faults are allowed to have relatively small mutual differences that could be concealed by other system uncertainties. 2) An adaptive threshold mechanism based sFI approach is proposed by developing a bank of sFI estimators through a novel mechanism of absolute measurement of fault dynamics differences, which are capable of capturing the small fault differences and distinguishing them from system uncertainties, and are able to overcome the aforementioned sign issue suffered by existing FI approaches. Where dζ is the constant number satisfying 0 < dζ < dζ∗ with dζ∗ being the size of the NN approximation region to be specified later
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
