Continuous and reliable process monitoring using wireless vibration sensors part Ⅰ
Factory automation and overall efficiency have always been a huge concern, not only because productivity gains can bring positive benefits, but equally important, it can reduce or eliminate the serious losses caused by equipment downtime. Now, instead of relying on advances in analytical techniques to gain insight into available statistics to predict maintenance needs, or simply rely on enhanced training for technicians, we can achieve true real-time analysis and control through inspection and advances in wireless transmission technology.
Precision industrial processes rely more and more on the efficient, reliable and consistent operation of motors and related machinery. Unbalanced machinery, equipment, loose fasteners, and other anomalies often translate into vibration, resulting in reduced accuracy and safety issues. If left unchecked, in addition to performance and safety issues, if the equipment is shut down for repairs, it will inevitably lead to loss of productivity. Even with minor changes in equipment performance, this is often difficult to predict in time and quickly translates into significant productivity losses.
Process monitoring and state-based predictive maintenance are well known as an effective way to avoid loss of productivity, but the complexity of this approach is comparable to its value. Existing methods have limitations, particularly when it comes to analyzing vibration data (however it is obtained) and determining the source of the error.
Typical data acquisition methods include simple piezoelectric sensors and handheld data acquisition tools installed on the machine. These methods have a number of limitations, especially when compared to an ideal comprehensive detection and analysis system solution that can be embedded on a machine or machine and can work autonomously. The following is an in-depth discussion of these limitations and their comparison with the ideal solution, the autonomous wireless embedded sensor. Option analysis of complex system objectives for fully embedded autonomous detection components can be divided into ten different aspects, including achieving high repeatability measurements, accurately evaluating acquired data, proper documentation, and traceability. In the next section we will explain and explore the available methods and ideal methods in detail.