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DOI: 10.1007/s10033-017-0122-4

Pulsed Eddy Current Non-destructive Testing and Evaluation: A Review
Ali Sophian, Guiyun Tian and Mengbao Fan

Abstract: Pulsed eddy current (PEC) non-destructive testing and evaluation (NDT&E) has been around for some time and it is still attracting extensive attention from researchers around the globe, which can be witnessed through the reports reviewed in this paper. Thanks to its richness of spectral components, various applications of this technique have been proposed and reported in the literature covering both structural integrity inspection and material characterization in various industrial sectors. To support its development and for better understanding of the phenomena around the transient induced eddy currents, attempts for its modelling both analytically and numerically have been made by researchers around the world. This review is an attempt to capture the state-of-the-art development and applications of PEC, especially in the last 15 years and it is not intended to be exhaustive. Future challenges and opportunities for PEC NDT&E are also presented.

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https://cjme.springeropen.com/articles/10.1007/s10033-017-0122-4

DOI: 10.1007/s10033-017-0189-y

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Si-Yu Shao, Wen-Jun Sun, Ru-Qiang Yan, Peng Wang and Robert X Gao

Abstract: Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

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https://cjme.springeropen.com/articles/10.1007/s10033-017-0189-y

DOI: 10.1186/s10033-018-0294-6

High Resolution Clinometers for Measurement of Roll Error Motion of a Precision Linear Slide

Yuki Shimizu, Satoshi Kataoka and Wei Gao

Abstract: The surface quality of chamfer milling of stainless steel is closed related to the products of 3C (Computer, Communication and Consumer electronics), where a cutter is a major part to achieve that. Targeting a high-quality cutter, an experimental evaluation is carried out on the influence of grinding texture of cutter flank face on surface quality. The mathematic models of chamfer cutter are established, and they are validated by a numerical simulation. Also the grinding data are generated by the models and tested by a grinding simulation for safety reasons. Then, a set of chamfer cutting tools are machined in a five-axis CNC grinding machine, and consist of five angles between the cutting edge and the grinding texture on the 1st flank faces, i.e., 0°, 15°, 30°, 45° and 60°. Furthermore, the machined cutting tools are tested in a series of milling experiments of chamfer hole of stainless steel, where cutting forces and surface morphologies are measured and observed. The results show that the best state of both surface quality and cutting force is archived by the tool with 45° grinding texture, which can provide a support for manufacturing of cutting tool used in chamfer milling.

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https://cjme.springeropen.com/articles/10.1186/s10033-018-0294-6

DOI: 10.1007/s10033-017-0192-3

An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

De Z. Li, Wilson Wang and Fathy Ismail

Abstract: Induction motors (IMs) are commonly used in various industrial applications. To improve energy consumption efficiency, a reliable IM health condition monitoring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is proposed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are synthesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air-gap eccentricity diagnosis. The effectiveness of the proposed harmonic synthesis technique is examined experimentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.

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https://cjme.springeropen.com/articles/10.1007/s10033-017-0192-3

DOI: 10.1007/s10033-017-0186-1

Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

Yi-Fan Li, MingJian Zuo, Ke Feng and Yue-Jian Chen

Abstract: The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper presents a novel signal processing scheme, adaptive morphological update lifting wavelet (AMULW), for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration signals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective rolling element bearings.

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https://cjme.springeropen.com/articles/10.1007/s10033-017-0186-1

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