Any energy conversion devices, such as industrial motor-drives, propulsion drives of electric vehicles, pump systems, wind turbines, and others, are prone to failures. Usually, failures result in increased economic costs that come through additional energy losses, loss of production, or in a worst-case even environmental hazard. To prevent failures, energy conversion systems may be checked through particular routines developed and specified by the manufactures. However, it may be challenging due to the complex construction of energy conversion devices or devices' failure between the routine checks. Such schedule-based condition monitoring approaches provide minor information on the remaining lifetime (separate components and whole system) of the devices and do not allow proper prognostic or full exploitation. To overcome traditional two-level Boolean approaches with healthy/faulty states an Artificial Intelligence (AI)-based control techniques are used. The Fuzzy Logic approach is based on inspired by human perception processes and cognition that are often uncertain or empirical. However, Fuzzy Logic is already successfully applied in various control applications of energy conversion devices, even when the analytical models are unknown. This paper argues for developing new fault detection algorithms based on fuzzy logic methods to allow energy conversion systems designers to develop reliability factors for apparatus, which included electrical machines and power electronics subsystems.

An Overview of Fuzzy Logic Approaches for Fault Diagnosis in Energy Conversion Devices

Diagnosis of Nonlinear Delay Systems

The general theme of this article concerns the diagnosis of nonlinear delay systems. Parity space and observers approaches are used to generate residuals. To accomplish fault isolation, we use a bank of observers for all possible faulty models. The paper considers constant actuator faults, that is, the outputs of some actuators are stuck at fixed undesirable constant values. The fault isolation strategy is based on combining the conventional observer design techniques with adaptation techniques. Based on the designed observers, a bank of residuals are defined correspondingly. The actuator faults can be isolated if only one residual goes to zero while the others do not. The faulty model with residual approaching zero identifies the faulty actuators. For linear and nonlinear systems with all states available, new sufficient conditions for fault isolation are derived, which require only that the distribution matrices of the actuators are of full column rank.

Presents a dynamic fuzzy model (DFM) based fault detection and isolation (FDI) scheme for the nonlinear system. The dynamic behavior of a nonlinear process is represented by a fuzzy aggregation of a set of local linear models. The parameter variations of the DFM identified in online and the validity of the local linear models are used to generate a residual vector. The neural network classifier, which learned the relationship between the residual vector and fault type, detects and isolates the process faults. We apply the proposed FDI scheme to the design of an FDI system of a two-tank system and show the usefulness of the proposed scheme.

Fault diagnosis of nonlinear system based on fuzzy dynamic model

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Fault diagnosis of nonlinear system based on fuzzy dynamic model