Analysis of Ball Bearing Defects in Synchronous Machines using Electrical Measurements

Rolling element bearings are used in most electrical machines, especially for small and medium size applications. Under non-ideal operating conditions, ball bearing condition degrades by fatigue, ambient vibration, misalignment, overloading, contamination, corrosion from water or chemicals, improper lubrication, shaft currents and residual stress left from the bearing manufacturing process. All of these conditions eventually lead to increased vibration and acoustic noise during machine operation which at some point in time results in unexpected bearing failure. Over the years, a great number of publications have been devoted to the detection of mechanical faults, including rolling element bearing defects and torsional defects, in electrical machines based on Electrical Signature Analysis (ESA). It has been observed that these faults can affect either the stator to rotor air-gap distribution or the running speed of the machine, which can be reflected in the signature of the electrical signals. However, the physical link between the mechanical degradation and the electrical signature is still not explained well. A multi-physics model is developed by joining the detailed mechanical model of a rotor bearing system and the electrical model of a synchronous machine in this research. This combined model is capable of describing the transmission of information originating from bearing faults and their impact on the variations of the measured electrical signals. The electrical machine model is developed based on winding function approach and its validity is demonstrated by a more accurate Finite Element Method (FEM) model. The mechanical model consists of a high fidelity rotor-bearing system with detailed nonlinear ball bearing model and a flexible finite element shaft model. It is validated using the housing vibration data collected from some experiments. Generalized roughness bearing anomalies are linked to load torque ripples and airgap variations, while being related to current signature by phase and amplitude modulation. Considering that the induced characteristic signatures are usually subtle broadband changes in the current spectra, these signatures are easily affected by input power quality variations, machine manufacturing imperfections and environmental noise. In this research, a new algorithm is proposed to isolate the influence of the external disturbances of power quality, machine manufacturing imperfections and environmental noise, and to improve the effectiveness of applying the ESA for generalized roughness bearing defects. The results show that the proposed method is effective in analyzing the generalized roughness bearing anomaly in synchronous machines. Furthermore, the electrical signatures are analyzed in a synchronous machine with bearing defects. The proposed fault detection method employs a Zoomed Fast Fourier Transform (ZFFT) and Principal Component Analysis (PCA) and it is also tested on the available experimental data. The results show that amplitude induced electrical harmonics are related to the level of vibration, and the electrical signatures are affected heavily by other variables, such as power quality and load fluctuation. The proposed method is shown to be effective on detecting generalized roughness bearing defects in synchronous machines

Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China

The rapid development of Internet money funds (IMFs) may become the main development direction of money funds in the future. For the characteristics of IMFs return time series data with solid nonlinearity and poor smoothness, this study uses long and short-term memory (LSTM) neural network to predict IMFs return. By constructing a 1DCNN (one-dimensional convolutional neural network) and a self-attentive mechanism, the LSTM feature extraction capability is optimized, and an XGBOOST model is built after the output layer to construct a prediction error sequence to compensate for the original prediction sequence to achieve a correction effect. Finally, the trained model is applied to rolling forecast the 43-day return data of the actual trading days in the next two months, and the VaR method is applied to realize the IMF risk measure. The results displayed the following: (1) The 1DCNN-LSTM-SAM-XG has a significant improvement in accuracy compared with models such as LSTM neural network and SVR, and the MAPE values are reduced by 1.372% and 2.887%, respectively, indicating that the model established in this study is characterized by high accuracy and robustness. (2) According to the VaR methodology, the FUND series has the highest risk, the BANK series has the second highest risk, and the THIRD series has the lowest risk

H2O2-Enhanced Shale Gas Recovery under Different Thermal Conditions

The permeability of tight shale formations varies from micro-Darcy to nano-Darcy. Recently, hydrogen peroxide (H2O2) was tested as an oxidizer to remove the organic matter in the rock in order to increase shale permeability. In this study, shale particles were reacted with hydrogen peroxide solutions under different temperature and pressure conditions in order to “mimic” underground geology conditions. Then, low-temperature nitrogen adsorption and desorption experiments were conducted to measure the pore diameters and porosity of raw and treated shale samples. Moreover, scanning electron microscopy (SEM) images of the samples were analyzed to observe pore structure changes on the surface of shale samples. From the experiments, it was found that the organic matter, including extractable and solid organic matter, could react with H2O2 under high temperature and pressure conditions. The original blocked pores and pore throats were reopened after removing organic matter. With the increase of reaction temperature and pressure, the mean pore diameters of the shale samples decreased first and then increased afterwards. However, the volume and Brunauer–Emmett–Teller (BET) surface areas of the shale particles kept increasing with increasing reaction temperature and pressure. In addition to the effect of reaction temperature and pressure, the pore diameter increased significantly with the increasing reaction duration. As a result, H2O2 could be used to improve the shale permeability

Design of thermal wind sensor with constant power control and wind vector measurement method.

This paper presents a conceptual wind vector detector for measuring the velocity and direction of wind in enclosed or semi-enclosed large spaces. Firstly, a thermal wind sensor with constant power control was manufactured and then used as a wind velocity sensing unit. Secondly, a sensor bracket equipped with three thermal wind sensors was designed, the fluid dynamic response regularity of the measured wind field to the sensor bracket was analyzed using ANSYS Fluent CFD software, and then its structural parameters were optimized to improve measurement accuracy. The sensor bracket was fabricated via 3D printing. Finally, a unique wind vector measurement method was developed for the wind vector detector. Experimental results showed that the measured velocity range of the thermal wind sensor satisfied the requirements of being within 0-15 m/s with an accuracy of ±0.3 m/s, and the wind direction angle range of the wind vector detector was within 0-360° with an accuracy of ±5°. By changing the applied power control value of the thermal wind sensor and structural parameters of the sensor bracket, the measurement range and accuracy of the wind vector detector can be adjusted to suit different applications