Vibration Signal Forecasting on Rotating Machinery by means of Signal Decomposition and Neurofuzzy Modeling

Abstract

Vibration monitoring plays a key role in the industrial machinery reliability since it allows enhancing the performance of the machinery under supervision through the detection of failure modes. Thus, vibration monitoring schemes that give information regarding future condition, that is, prognosis approaches, are of growing interest for the scientific and industrial communities. This work proposes a vibration signal prognosis methodology, applied to a rotating electromechanical system and its associated kinematic chain. The method combines the adaptability of neurofuzzy modeling with a signal decomposition strategy to model the patterns of the vibrations signal under different fault scenarios. The model tuning is performed by means of Genetic Algorithms along with a correlation based interval selection procedure. The performance and effectiveness of the proposed method are validated experimentally with an electromechanical test bench containing a kinematic chain. The results of the study indicate the suitability of the method for vibration forecasting in complex electromechanical systems and their associated kinematic chains.

Vibration‐Based Fault Diagnosis of Commutator Motor

“…e acquired results were similar to other proposed techniques of vibration analysis [11,20,[34][35][36][37]. In [11], results with an error lower than 2% were obtained. However, they [11] analysed bearing failure, 1/2-broken rotor bar, and 1 broken rotor bar of the induction motor.…”

“…It is also noninvasive technique of fault diagnosis. e acquired results are similar to other proposed techniques of fault diagnosis [1,11]. In the paper, original methods of feature extraction-MSAF-RATIO-50-SFC and MSAF-RATIO-50-SFC-EXPANDED were used for vibration signals.…”

“…Condition monitoring of electric motors was developed for measurement and analysis of diagnostic signals such as acoustic [1,2], thermal [3,4], electric current [5][6][7], and vibration [8][9][10][11][12][13]. Each type of signal has advantages and disadvantages.…”

“…e authors indicated that multisensors information fusion is the trend of development [10]. Zurita-Millan et al [11] proposed a vibration signal prognosis methodology of the electromechanical system (kinematic chain). e proposed methodology was based on neurofuzzy modeling using the patterns of the vibrations signal.…”

“…ey proved that the RMS method is a proper feature for vibration analysis. ey obtained the results with an error lower than 2%, but they did not analyse other sources of information, such as temperature and stator current [11]. Lu et al described vibration-based condition monitoring of motor bearings [12].…”