Rotary machine vibration monitoring and smart balance correction

During the rotary machine operation process, seemingly small amounts of abnormal vibration can often cause serious damage to the machinery over time and even increase the risk of accidents. Although professional vibration engineers can determine the current health status of a machine by interpreting the vibration spectrum information and predicting which components will fail, if even ordinary operators can send feedback regarding the vibration signals reaching the human–machine interface through a system when an abnormality is detected in the machine, the abnormality can be made known and processed in time. This can prevent the magnified impact of rotary inertia, thereby lowering the risk of major damage and the failure of machinery and equipment, as well as effectively saving on equipment maintenance costs. This study mainly adopted LabVIEW and Arduino IDE to develop a control program and human–machine monitoring interface. As the initial experiment on rotary machine vibration monitoring and smart balance correction, the measurement system setup in this study was applied to determine vibration abnormality as well as to carry out continuous online automatic balance correction. Experimental verification was carried out using active correction and smart correction. In terms of active online balance correction, the amplitude correction rate was 96%, the double-frequency correction rate was 102.9%, and the correction process was performed in 5 min. In terms of smart balance correction, the amplitude correction rate was 103.8%, the double-frequency correction rate was 103.3%, and the correction process was performed in 3 min. Through feedback signaling, the operator can effectively learn the current health status of the mechanical equipment from the human–machine interface.

Convergence of explicitly coupled simulation tools (co-simulations)

In engineering, it is a common desire to couple existing simulation tools together into one big system by passing information from subsystems as parameters into the subsystems under influence. As executed at fixed time points, this data exchange gives the global method a strong explicit component. Globally, such an explicit co-simulation schemes exchange time step can be seen as a step of an one-step method which is explicit in some solution components. Exploiting this structure, we give a convergence proof for such schemes. As flows of conserved quantities are passed across subsystem boundaries, it is not ensured that system-wide balances are fulfilled: the system is not solved as one single equation system. These balance errors can accumulate and make simulation results inaccurate. Use of higher-order extrapolation in exchanged data can reduce this problem but cannot solve it. The remaining balance error has been handled in past work by recontributing it to the input signal in next coupling time step, a technique labeled balance correction methods. Convergence for that method is proven. Further, the lack of stability for co-simulation schemes with and without balance correction is stated.