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A fault detection, isolation, and estimation approach is proposed in this paper based on Interactive Multimodel (IMM) fusion filtering and Strong Tracking Filtering (STF) for asynchronous multisensors dynamic systems. Time-varying fault is considered and a candidate fault model is built by augmenting the unknown fault amplitude directly into the system state for each kind of possible fault mode. By doing this, the dilemma of predetermining the fault extent as model design parameters in traditional IMM-based approaches is avoided. After that, the time-varying fault amplitude is estimated based on STF using its strong ability to track abrupt changes and robustness against model uncertainties. Through fusing information from multiple sensors, the performance of fault detection, isolation, and estimation is approved. Finally, a numerical simulation is performed to demonstrate the feasibility and effectiveness of the proposed method.

In recent years, modern engineering systems have become huge in investment, large in scale, and more and more sophisticated in structure. As a result, faults in these complex systems may lead to enormous losses. Consequently, fault detection and diagnosis (FDD) has attracted more and more attentions as an effective method to reduce the accident risk and enhance the security of systems [

On the other hand, with the rapid development of sensor techniques, the number and type of sensors used for system monitoring in modern engineering systems are increased greatly [

The aim of this paper is to study the time-varying fault detection, isolation, and estimation problem of stochastic dynamic systems with multiple asynchronous sensors. In existing IMM-based FDD approaches, the unknown fault extent presents as model parameters in candidate fault model and needs to be predetermined in the process of model set design. Meanwhile, since fault extent actually takes value from a continuous interval, several fault models with distinct fault extents for a given kind of fault need to be included in the model set in order to have satisfactory coverage of all possible fault conditions. Different from existing approaches, the proposed FDD strategy in this paper regards the fault amplitude as unknown state variables and augments it directly into the system state to build the candidate fault model. By doing this, for each kind of fault, only one fault model is needed and the dilemma of predetermining the fault extent as model parameters in the model set design process is avoided. Then the asynchronous IMM fusion filtering is performed to the model set consisting of normal model and augmented fault models, and the fault is detected and isolated simultaneously based on the posterior model probabilities. Finally, STF is utilized to jointly estimate the system state and time-varying fault amplitude by fusing all asynchronous measurements from sensors and making use of its strong robustness to model uncertainties.

The rest of this paper is organized as follows. A description of time-varying FDD problem for stochastic dynamic systems with asynchronous sensors is presented in Section

Consider the following continuous-time linear dynamic system:

Let

In this paper, time-varying fault

As we said above, for fault detection and isolation, an augmented IMM is used in this paper. The model set design of the augmented IMM is introduced in this section.

When the

A complete cycle of the IMM-based-FDI scheme is discussed below.

Given

From the state transition equation (

Substituting (

Then we define

Consequently, in the case of

Equivalent measurement noise

At the same time, the equivalent measurement noise

Now for the system composed of the augmented state equation (

The model probability of the

Finally from the total probability formula, we have

Once

The first four steps above constitute the proposed fusion IMM algorithm. The augmented state is estimated under each possible current model through fusing asynchronous measurements from

Once the fault is successfully detected and isolated, the STF could be used to track the development of fault amplitude using its strong ability to track abrupt changes and strong robustness to model mismatch [

In this section, simulation results are provided to verify the proposed algorithm. Consider the dynamic system described by (

There exist two actuators in this system. Here we assume that Actuator 2 has a time-varying fault which occurs at

Model probability.

RMSE curves of state estimation.

RMSE curves of fault amplitude.

In this paper, we have presented a new approach for detecting, isolating, and estimating time-varying fault based on IMM and STF for asynchronous multisensor systems. An augmented IMM has been performed by augmenting the fault amplitude directly into the state vector, and the model probability generated by the augmented IMM has been used to detect and isolate the fault, which is superior to other model-based fault detection methods in that it has a clear detection threshold, while the fault estimation has been achieved based on STF which has good tracking performance for the time-varying fault, even when abrupt changes exist. There are no constraints on the number of the sensors or the initial sample times and sampling rates of multiple sensors. The simulation results demonstrate that the proposed algorithm can detect fault quickly and estimate it accurately. Further works can focus on the fault diagnosis problems on networked systems and systems with state constraints.

The authors declare that there are no conflicts of interest regarding the publication of this paper.

This work was supported by the National Natural Science Foundation of China under Grant 61773055, in part by the Fundamental Research Funds for the Central Universities of China with Grant FRF-TP-16-029A3, and in part by the Beijing Key Laboratory of Knowledge Engineering for Materials Science under Grant FRF-BD-16-010A.