PEM fuel cell fault diagnosis via a hybrid methodology based on fuzzy and pattern recognition techniques

Abstract

a b s t r a c tIn this work, a fault diagnosis methodology termed VisualBlock-Fuzzy Inductive Reasoning, i.e. VisualBlock-FIR, based on fuzzy and pattern recognition approaches is presented and applied to PEM fuel cell power systems. The innovation of this methodology is based on the hybridization of an artificial intelligence methodology that combines fuzzy approaches with well known pattern recognition techniques. To illustrate the potentiality of VisualBlock-FIR, a non-linear fuel cell simulator that has been proposed in the literature is employed. This simulator includes a set of five fault scenarios with some of the most frequent faults in fuel cell systems. The fault detection and identification results obtained for these scenarios are presented in this paper. It is remarkable that the proposed methodology compares favorably to the model-based methodology based on computing residuals while detecting and identifying all the proposed faults much more rapidly. Moreover, the robustness of the hybrid fault diagnosis methodology is also studied, showing good behavior even with a level of noise of 20 dB.

Hydrogen Leakage Diagnosis for Proton Exchange Membrane Fuel Cell Systems: Methods and Suggestions on Its Application in Fuel Cell Vehicles

“…In addition, many scholars used the model-based diagnosis methods to diagnose the gas leakage from fuel cell air supply system [39][40][41][42][43], which provide important references for the design of hydrogen leakage fault diagnosis methods. Kamal et al [39] established an independent radial basis function network model.…”

“…In the existing research, there are many diagnosis methods, either for the hydrogen leakage from the hydrogen supply system or for the hydrogen leakage in the stack. As shown in Figure 1, these methods used different strategies to diagnose hydrogen leakage, mainly including environmental hydrogen concentration diagnosis [24][25][26][27][28][29][30], hydrogen pressure decay diagnosis [9,10,[32][33][34]100,[112][113][114], gas leakage diagnosis by model-based methods or data-driven methods [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50]115,123], cross-current diagnosis [10,[100][101][102][103], and diagnosis by stack (or cells) output voltage [10,100,[104][105][106][107][108]…”

A Fuzzy Virtual Actuator for Automated Guided Vehicles

“…A fuzzy residual evaluation is proposed by Gentil et al [ 43 ]. Escobet et al [ 44 ] employ a hybrid methodology based on fuzzy and pattern recognition techniques for fault diagnosis. An active sensor fault-tolerant output feedback tracking control is proposed by Shaker and Patton [ 45 ].…”

Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

“…On the one hand, model-based diagnosis uses a mathematical model to simulate system variables during normal operation and to generate residuals by comparing the simulated variables with those measured on the system [5,7,8,9,12,13,14]. On the other hand, signal-based approaches directly treat measured signals to extract information and define different patterns representative of variable behaviour during both normal and faulty conditions [15,16,17,18,19].…”