This paper addresses a new model-based fault detection, estimation, and prediction scheme for linear distributed parameter systems (DPSs) described by a class of partial differential equations (PDEs). An observer is proposed by using the PDE representation and the detection residual is generated by taking the difference between the observer and the physical system outputs. A fault is detected by comparing the residual to a predefined threshold. Subsequently, the fault function is estimated, and its parameters are tuned via a novel update law. Though state measurements are utilized initially in the parameter update law for the fault function estimation, the output and input filters in the modified observer subsequently relax this requirement. The actuator and sensor fault functions are estimated and the time to failure (TTF) is calculated with output measurements alone. Finally, the performance of detection, estimation and a prediction scheme is evaluated on a heat transfer reactor with sensor and actuator faults.
Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be used to remove the outliers. The OIR scheme uses a neural network (NN) to estimate the actual system states from measured system states involving outliers. With this method, the outlier detection is performed online at each time instant by finding the difference between the estimated and the measured states and comparing its median with its standard deviation over a moving time window. The NN weight update law in OIR is designed such that the detected outliers will have no effect on the state estimation, which is subsequently used for model-based fault diagnosis. In addition, since the OIR estimator cannot distinguish between the faulty or healthy operating conditions, a separate model-based observer is designed for fault diagnosis, which uses the OIR scheme as a preprocessing unit to improve the FD performance. The stability analysis of both OIR and fault diagnosis schemes are introduced. Finally, a three-tank benchmarking system and a simple linear system are used to verify the proposed scheme in simulations, and then the scheme is applied on an axial piston pump testbed. The scheme can be applied to nonlinear systems whose dynamics and underlying distribution of states are subjected to change due to both unknown faults and operating conditions.
Complex nonlinear systems such as an aircraft, trains, automobiles, power plants and chemical plants are represented as nonlinear interconnected subsystems. Therefore, in this paper a novel decentralized fault diagnosis and prognosis (FDP) methodology is proposed for such large-scale systems. Current FDP approaches require the knowledge of the entire state or its estimated vector. But the main goal in this work is to design a local fault detector (LFD) or observer for each subsystem based on the measured local states of the subsystem alone. A local residual signal is generated via the measured states of the local subsystem and the estimated states provided by the LFD. A fault is detected when this local residual exceeds a predefined threshold. The adaptive online approximator in each LFD is activated upon detection to compensate the fault dynamics due to local and non-local faults. A novel update law for tuning the parameters of the online approximator is derived. Upon detection, faults local to the subsystem and to other subsystems are isolated. In addition, the proposed scheme provides the time to failure (or remaining useful life) information by using local measurements and the parameter update law of the LFD. Simulation results verify the effectiveness of the proposed decentralized FDP scheme.
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