Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy.

Evaluation of wet and dry event’s trend and instability based on the meteorological drought index

A temporal imbalance in the water availability, which is consistently below average or more than average rainfall, can lead to extremely dry or wet conditions. This impacts on agricultural yields, water resources and human activities. Weather instabilities and trends of wet/dry events have not yet been explored in Pakistan. In this study, we have two-fold objectives: (1) evaluate the weather instabilities, and (2) the trend of dry/wet events of selected stations of Pakistan. To observe weather instabilities, we used Mean Marginal Hilbert Spectrum (MMHS) and Continuous Wavelet Power Spectrum (CWPS) as meteorological series are mostly non-linear and non-stationary. We used Ensemble Empirical Mode Decomposition (EEMD) for the analysis of temporal characteristics of dry/wet events. We found that all stations are facing severe weather instabilities during the short period of 5 and 10 months using MMHS method and CWPS has shown the weather instabilities during 4 to 32 months of periodicity for all stations. Ultimately, the achieved short-term weather instabilities indicated by MMHS is consistent with CWPS. In summary, these findings might be useful for water resource management and policymakers.

Comparative Study on RAOs of a Ship Under Transient Gaussian Wave Packets by Marginal Hilbert Spectrum and Fourier Spectrum

Sea-keeping model tests of ships based on transient waves have been widely applied over the past several decades. In order to obtain response amplitude operators (RAOs) of a ship, most of the post-processing of the experimental data uses the fast Fourier transform (FFT) to obtain the wave spectrum and the corresponding response spectrum. However, for transient waves related model tests, FFT may produce larger errors due to its characteristics. Hilbert-Huang transform (HHT) is a newly developed signal analysis tool which is suitable for nonlinear and non-stationary data. The application of HHT to the post-processing of the experimental data of sea-keeping model tests of ships has not yet been investigated. In this study, the transient wave packets satisfying a Gaussian wave spectrum were generated in a large towing tank to conduct the sea-keeping model tests of a drilling ship under the condition of head waves, oblique waves and beam waves, respectively. Then the marginal Hilbert spectrum (MHS) in the framework of HHT is introduced to obtain the motion and the acceleration RAOs the drilling ship. In order to demonstrate the effectiveness of the approach, the results based on FFT and regular waves are also presented. It is found that in most cases, in comparison to that by means of FFT, the RAOs of the ship based on the transient Gaussian wave packets by means of MHS agree better with the results based on regular waves, especially for roll motion with significant nonlinear characteristics. Due to the advantages of HHT, the MHS approach employed in this study is expected to play a vital role in more sea-keeping related model tests of ships.

End-to-End Noisy Speech Recognition Using Fourier and Hilbert Spectrum Features

Despite the progress of deep neural networks over the last decade, the state-of-the-art speech recognizers in noisy environment conditions are still far from reaching satisfactory performance. Methods to improve noise robustness usually include adding components to the recognition system that often need optimization. For this reason, data augmentation of the input features derived from the Short-Time Fourier Transform (STFT) has become a popular approach. However, for many speech processing tasks, there is an evidence that the combination of STFT-based and Hilbert–Huang transform (HHT)-based features improves the overall performance. The Hilbert spectrum can be obtained using adaptive mode decomposition (AMD) techniques, which are noise-robust and suitable for non-linear and non-stationary signal analysis. In this study, we developed a DeepSpeech2-based recognition system by adding a combination of STFT and HHT spectrum-based features. We propose several ways to combine those features at different levels of the neural network. All evaluations were performed using the WSJ and CHiME-4 databases. Experimental results show that combining STFT and HHT spectra leads to a 5–7% relative improvement in noisy speech recognition.