Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

Fault Diagnosing of Cycloidal Gear Reducer Using Statistical Features of Vibration Signal and Multifractal Spectra

The article presents a method for diagnosing cycloidal gear damage on a laboratory stand. The damage was simulated by removing the sliding sleeves from two adjacent external pins of the cycloidal gearbox. Damage to the sliding sleeves may occur under operating conditions and can lead to the destruction of the gear unit. Hence, early detection is essential. Signals from torque sensors, rotational speed sensors and vibration acceleration sensors of input and output shafts for various rotational speeds and transmission loads were recorded. The frequency analysis of these signals was carried out. Due to the fluctuation of the rotational speed, the frequency spectrum gives an approximate picture and is not useful in detecting this type of damage. The statistical characteristics of the signal were determined. However, only statistical moments of higher orders, such as kurtosis, are sensitive to the tested damage. Therefore, the use of multifractal analysis of the vibration signal using the wavelet leader method (WLMF) was considered. Then log-cumulants of the multifractal spectrum were selected as the new signal features.

Power Regulation and Fault Diagnostics of a Three-Pond Run-of-River Hydropower Plant

Hydropower generation is one of the most prominent renewable sources of power. Run-of-river hydropower is like traditional hydropower but has significantly less environmental impact. Faults in industrial processes are a cause for large amounts of losses in monetary value and off times in industrial processes and consumer utilities. It is more efficient for the system to identify the occurring faults and, if possible, to have the processes running without interruption with the occurrence of a fault. This work uses a model previously proposed—the three-pond hydraulic run-of-river system and integrates it with a turbine and regulated power generation. After integration of the hydraulic system with the turbine and power generation, we then design a diagnostic system for commonly occurring faults within the system. Mathematical models of the faults are formulated and residues are calculated. Fault detection and identification is achieved by analyzing the residues and then a fault-tolerant control is proposed. The Fault Diagnostic Module can correctly detect the faults present and offers sufficient fault compensation to make the system run nearly normally in the event of fault occurrence. With the emergence of distributed power generation smart grids and renewable energy, this fault diagnostic is able to reliably offer uninterrupted power to the grid and thus to consumers.

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.