Inter-turn short-circuit (ITSC) faults are the most common fault types of brushless DC (BLDC) motors used in industry. Fault diagnosis of BLDC motors is of critical importance due to their wide spread applications. Existing diagnosis methods are based on voltage and current analysis which is useful but difficult to identify early faults and fault locations, as these suffer from external disturbances. This article proposes a new offline method for fault detection based on magnetic leakage flux (MLF) and backpropagation neural network (BPNN) for improving the level of fault diagnosis. The ITSC and MLF are modeled and analyzed theoretically. Then they are verified by finite element analysis (FEM) and experimental tests. Hall sensors are used to form an array to collect MLF signals at different positions outside the test motor. The frequency-domain characteristic matrix of MLF signals is analyzed by BPNN models. The experimental results show that the proposed method can effectively detect ITSCs, and estimate the fault degree and the location of the fault. The method is a promising technology as it is non-intrusive and accurate.
Interpretation of depositional environments combined with field measurement of permeability for a portion of the Upper Cretaceous Straight Cliffs Formation near Escalante, Utah, provides new results for understanding and modeling facies-dependent perme- ability variations. Offshore, transition-lower shoreface, upper shore- face, and foreshore environments are interpreted for part of the John Henry Member on the basis of outcrop investigation. Using a newly designed drillhole minipermeameter probe, permeability was measured for two of the facies within this unit: lower-shoreface bioturbated sand- stone and upper-shoreface cross-bedded sandstone. Approximately 500 permeability measurements at a sample spacing of 15 cm were made along four vertical profiles and three horizontal transects o na6m 3 21 m outcrop. Permeability ranges from 41 to 1,675 millidarcies (md) in the bio- turbated sandstone facies, which is massive-bedded, moderately to well-sorted, and very fine- to fine-grained. In contrast, permeability ranges from 336 to 5,531 md in the moderately to moderately well sorted, fine- to medium-grained, cross-bedded sandstone facies. A high degree of variability in permeability of the cross-bedded facies is caused by small-scale variations in grain size and structure related to depositional processes. The geometric mean permeability in the bio- turbated sandstone is 253 md, compared with 1,395 md in the cross- bedded facies. While the facies-dependent differences in permeability (k) are ap- parent and related to depositional and biological processes, fractal- based statistical analysis of the horizontal ln( k) increments yields near- ly identical results for the bioturbated facies and the cross-bedded fa- cies, possibly suggesting an underlying statistical commonality in the formation of both facies. Increment distributions from both facies ap- pear similar with peaking around the mean. Ln(k) increments from the smaller vertical data set appear similar also, but with a variance approximately 3.7 times larger than the horizontal value. Variance scaling analyses of horizontal and vertical data both yield a Hurst co- efficient near 0.26, which is characteristic of negative spatial correla- tion of the increments. The methodology developed herein offers a po- tential high-resolution alternative to existing methods for understand- ing and characterizing subsurface properties.
Angle of rotation is a key parameter in motor fault diagnosis under varying speed conditions, and is usually measured by an optical encoder. However, the use of encoders is intrusive and in many scenarios its signal is difficult to access due to technical or commercial reasons. In this study, a novel rotation angle measurement method based on stray flux analysis is proposed and applied to bearing fault diagnosis of brushless direct-current (BLDC) motors. The measurement accuracy of the proposed method is comparable to that from an encoder. The developed method is flexible, noninvasive, and nondestructive. It is easy to implement and eliminates the need for long cables and access of the motor control system. The proposed method can be extended to the diagnosis of motor electrical and drive faults. If implemented with an Internet of Things (IoT) or a hand-held device, it can further improve the reliability of sensorless motor drive systems in industrial automation so as to meet Industry 4.0 requirements.
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