Setting Up a Vibration Monitoring Programme for Manufacturing Industries (original) (raw)
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Machine Vibration Monitoring for Diagnostics through Hypothesis Testing
Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher's Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes.
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The Journal of the Acoustical Society of America, 2002
Machine vibration is essentially the forward and backward development of machines or machine segments. Any segment that moves forward and backward or wavers is vibrating. Machine vibration can capture in different structures. All the more regularly, vibration is bothersome, squandering vitality and making the undesirable sound. Vibration investigation is an instrument to recognize, avert and anticipate disappointments in pivoting apparatus, for example, spiral boring machine, machine, and processing machine and so on. Actualizing vibration examination on these machines will enhance the unwavering quality of the machines and lead to all the more likely machine proficiency and diminished downtime disposing of mechanical or electrical part disappointments. Regular machines incorporate engines, siphons, fans, outfit boxes, blowers, turbines, transports, rollers, motors and mechanical devices having rotational components. The turning components of these machines produce vibrations at explicit frequencies that recognize the pivoting components capacities. In the present work, it is proposed to break down the vibration on spiral boring machine utilizing a piezoelectric sensor. Piezoelectric sensors are case mounted utilizing a perpetual jolt, convenient magnet or cement to hold them set up. They will gauge the vibration of the machine and deliver yield as a voltage or current corresponding to the vibration.
MATEC Web of Conferences, 2015
Vibration analysis is a powerful and widely used tool in industry for monitoring rotating machinery. Despite the possibilities offered by the measuring equipment of vibration through technological progress, companies which practice Vibration Based Analysis (VBM) show performance below expectations in terms of machine availability. Our duty then is to develop an approach for the implementation of the VBM taking into consideration the keys to success and avoiding the causes of failure. We carried out an analysis of 30 years of practice of the VBM within a large Moroccan company in the chemical industry, through a collective approach called DCA (in French applied short diagnosis) and a survey among national and international experts in the field of the VBM. The synthesis of these studies has led us to propose a comprehensive and structured approach that comes in 5 phases: inventory, feasibility studies, preparation phase, implementation phase, evaluation and improvement phase.