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Remote monitoring and fault diagnosis of an industrial machine through sensor fusion

University of British Columbia

Fault detection and diagnosis is quite important in engineering systems, and deserves further attention in view of the increasing complexity of modern machinery. Traditional single-sensor methods of fault monitoring and diagnosis may find it difficult to meet modern industrial requirements because there is usually no direct way to measure and accurately correlate a machine fault to a single sensor output. Fusion of information from multiple sensors can overcome this shortcoming. In this thesis, a neural-fuzzy approach of multi-sensor fusion is developed for a network-enabled remote fault diagnosis system. The approach is validated by applying it to an industrial machine called the Iron Butcher, which is a machine used in the fish processing industry for the removal of the head in fish prior to further processing for canning. An important characteristic of the fault diagnosis approach developed in this thesis is to make an accurate decision of the machine condition by fusing information from different sensors. First, sound, vibration and vision signals are acquired from the machine using a microphone, an accelerometer and a digital CCD camera, respectively. Second, the sound and vibration signals are transformed into the frequency domain using fast Fourier transformation (FFT). A feature vector from the FFT frequency spectra is defined and extracted from the acquired information. Also, a feature based vision tracking approach—the Scale Invariant Feature Transform (SIFT)—is applied to the vision data to track the object of interest (fish) in a robust manner. Third, Sound, vibration and vision feature vectors are provided as inputs to a neuro-fuzzy network for fault detection and diagnosis. A four-layer neural network including a fuzzy hidden layer is developed in the thesis to analyze and diagnose existing faults. By training the neural network with sample data for typical faults, faults of five crucial components in the fish cutting machine are detected with high reliability and robustness. Alarms to warn about impending faults may be generated as well during the machine operation. A network-based remote monitoring architecture is developed as well in the thesis, which will facilitate engineers to monitor the machine condition in a more flexible manner from a remote site. Developed multi-sensor approaches are validated using computer simulations and physical experimentation with the industrial machine, and compared with a single-sensor approach.

Thesis/Dissertation

application/pdf

2008-06-16

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/891

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/