Fault Diagnosis of an Industrial Machine Through Neuro-Fuzzy Sensor Fusion

In this paper a neuro-fuzzy approach of multi-sensor fusion is developed for a fault diagnosis system. The approach is validated by applying it to a machine called the Iron Butcher, which is used in industry for the removal of heads in fish prior to further processing for canning. An important goal of this approach developed in this paper is to make an accurate decision of the machine condition by fusing information from different sensors. Specifically, sound, vibration and vision measurements are acquired from the machine using a microphone, an accelerometer and a digital CCD camera, respectively. Next, the sound and vibration signals are transformed into the frequency domain using Fast Fourier Transform (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. In the diagnosis process, a candidate fish is detected and tracked. Sound, vibration and vision features are extracted as inputs for the neuro-fuzzy fault diagnosis system. A four-layer neural network including a fuzzy hidden layer is developed to analyze and diagnose any existing faults. By training the neural network with sample data for typical faults, six crucial faults in the fish cutting machine are detected precisely. In this manner, alarms to warn about impending faults may be generated as well during the machine operation. Developed approaches are validated using computer simulations and physical experimentation using the industrial machine.