[
    {
        "id": "osp-13224",
        "type": "article-journal",
        "title": "Neuro-Fuzzy Sensor Fault Diagnosis of an Induction Motor",
        "author": [
            {
                "family": "Benloucif",
                "given": "M. L."
            }
        ],
        "URL": "https://omanscience.com/ar/articles/neuro-fuzzy-sensor-fault-diagnosis-of-an-induction-motor",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2011
                ]
            ]
        },
        "container-title": "The Journal of Engineering Research",
        "volume": "7",
        "issue": "2",
        "page": "53",
        "DOI": "10.24200/tjer.vol8iss1pp53-60",
        "publisher": "Sultan Qaboos University",
        "ISSN": "1726-6009",
        "abstract": "In this paper, a neuro-fuzzy fault diagnosis scheme is presented and its ability to detect and isolate sensor faults in an induction motor is assessed. This fault detection and isolation (FDI) approach relies on a combination of neural modelling and fuzzy logic techniques which can deal effectively with nonlinear dynamics and uncertainties. It is based on a two step neural network procedure: a first neural network is used for residual generation and a second fuzzy neural network performs residual evaluation. Simulation results are given to demonstrate the efficiency of this FDI approach."
    }
]