Radial Vibration Measurement of Rotary Shafts through Electrostatic Sensing and Hilbert-Huang Transform

Radial vibration measurement of rotary shafts plays a significant part in condition monitoring and fault diagnosis of rotating machinery. This paper presents a novel method for radial vibration measurement through electrostatic sensing and HHT (Hilbert-Huang Transform) signal processing. The foundational characteristics of the electrostatic sensor in the vicinity of a drifting shaft are studied through Finite Element Modelling. Experimental tests were conducted on a purpose-built test rig to characterize the operating condition of the rotor at different rotational speeds (400 rpm and 600 rpm). A normal working shaft and an eccentric shaft were tested and the output signals from the electrostatic sensors were analyzed. Through empirical mode decomposition (EMD) on the electrostatic signals, the intrinsic mode functions (IMF) including the vibration information of the shaft are identified and further analyzed in the time-frequency domain. Experimental results suggest that the electrostatic sensing technique in conjunction with HHT provides a simple and cost-effective approach to radial vibration measurement of rotary shafts

Vibration Measurement of an Unbalanced Metallic Shaft Using Electrostatic Sensors

Vibration measurement of a rotary shaft is essential for the diagnosis and prognosis of industrial rotating machinery. However, the imbalance of a shaft, as quantified through vibration displacement, is the most common cause of machine vibration. The objective of this study is to develop a novel technique through electrostatic sensing for the on-line, continuous and non-contact displacement measurement of a rotary shaft due to imbalance faults. A mathematical model is established to extract useful information about the shaft displacement vibration from the simulated signal in the frequency domain. Experimental tests were conducted on a purpose-built test rig to measure the displacement vibration of the shaft. An eccentric shaft was tested with the output signal from the electrostatic sensor analyzed. The effectiveness of the proposed method is verified through computer simulation and experimental tests. Results obtained indicate that the measurement system yields a relative error of within ±0.6% in the displacement measurement

Electrostatic Sensors – Their Principles and Applications

Over the past three decades electrostatic sensors have been proposed, developed and utilised for the continuous monitoring and measurement of a range of industrial processes, mechanical systems and clinical environments. Electrostatic sensors enjoy simplicity in structure, cost-effectiveness and suitability for a wide range of installation conditions. They either provide unique solutions to some measurement challenges or offer more cost-effective options to the more established sensors such as those based on acoustic, capacitive, optical and electromagnetic principles. The established or potential applications of electrostatic sensors appear wide ranging, but the underlining sensing principle and resultant system characteristics are very similar. This paper presents a comprehensive review of the electrostatic sensors and sensing systems that have been developed for the measurement and monitoring of a range of process variables and conditions. These include the flow measurement of pneumatically conveyed solids, measurement of particulate emissions, monitoring of fluidised beds, on-line particle sizing, burner flame monitoring, speed and radial vibration measurement of mechanical systems, and condition monitoring of power transmission belts, mechanical wear, and human activities. The fundamental sensing principles together with the advantages and limitations of electrostatic sensors for a given area of applications are also introduced. The technology readiness level for each area of applications is identified and commented. Trends and future development of electrostatic sensors, their signal conditioning electronics, signal processing methods as well as possible new applications are also discussed

Rotational Speed Measurement through Image Similarity Evaluation and Spectral Analysis

Accurate and reliable measurement of rotational speed is desirable in a variety of industries. This paper presents a rotational speed measurement system based on a low-cost imaging device with a simple marker on the rotor. Sequential images are pre-processed through denoising, histogram equalization and circle Hough transform, and then processed by similarity evaluation methods to obtain the similarity level of images. Finally, the rotational speed is obtained through Chirp-Z transform on the restructured signals. The measurement principle, structure design and performance assessment of the proposed system are presented. The effects of different influence factors, including frame rate, marker shape and size, algorithm for image similarity evaluation, illumination conditions, shooting angle and photographic distance, on the performance of the measurement system are quantified and discussed through a series of experimental tests on a laboratory test rig. Experimental results suggest that the system is capable of providing constant rotational speed measurement with a maximum relative error of ±0.6% and a repeatability of less than 0.6% over a speed range from 100 to 900 RPM. Under varying speed conditions the proposed system can achieve valid measurement with a relative error within ±1% over the speed range of 300 to 900 RPM

Advances in Vibration Analysis Research

Vibrations are extremely important in all areas of human activities, for all sciences, technologies and industrial applications. Sometimes these Vibrations are useful but other times they are undesirable. In any case, understanding and analysis of vibrations are crucial. This book reports on the state of the art research and development findings on this very broad matter through 22 original and innovative research studies exhibiting various investigation directions. The present book is a result of contributions of experts from international scientific community working in different aspects of vibration analysis. The text is addressed not only to researchers, but also to professional engineers, students and other experts in a variety of disciplines, both academic and industrial seeking to gain a better understanding of what has been done in the field recently, and what kind of open problems are in this area

Cumulative index to NASA Tech Briefs, 1986-1990, volumes 10-14

Tech Briefs are short announcements of new technology derived from the R&D activities of the National Aeronautics and Space Administration. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This cumulative index of Tech Briefs contains abstracts and four indexes (subject, personal author, originating center, and Tech Brief number) and covers the period 1986 to 1990. The abstract section is organized by the following subject categories: electronic components and circuits, electronic systems, physical sciences, materials, computer programs, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings : a review

A rolling bearing is an essential component of a rotating mechanical transmission system. Its performance and quality directly affects the life and reliability of machinery. Bearings’ performance and reliability need high requirements because of a more complex and poor working conditions of bearings. A bearing with high reliability reduces equipment operation accidents and equipment maintenance costs and achieves condition-based maintenance. First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, and finally, the advantages, disadvantages, and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized. This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings.The Natural Science Foundation of China (NSFC) (grant numbers: 51675403, 51275381 and 51505475), National Research Foundation, South Africa (grant numbers: IFR160118156967 and RDYR160404161474), and UOW Vice-Chancellor’s Postdoctoral Research Fellowship.International Journal of Advanced Manufacturing Technology2019-04-01hj2018Electrical, Electronic and Computer Engineerin

Computing Intelligence Technique and Multiresolution Data Processing for Condition Monitoring

Condition monitoring (CM) of rotary machines has gained increasing importance and extensive research in recent years. Due to the rapid growth of data volume, automated data processing is necessary in order to deal with massive data efficiently to produce timely and accurate diagnostic results. Artificial intelligence (AI) and adaptive data processing approaches can be promising solutions to the challenge of large data volume. Unfortunately, the majority of AI-based techniques in CM have been developed for only the post-processing (classification) stage, whereas the critical tasks including feature extraction and selection are still manually processed, which often require considerable time and efforts but also yield a performance depending on prior knowledge and diagnostic expertise. To achieve an automatic data processing, the research of this PhD project provides an integrated framework with two main approaches. Firstly, it focuses on extending AI techniques in all phases, including feature extraction by applying Componential Coding Neural Network (CCNN) which has been found to have unique properties of being trained through unsupervised learning, capable of dealing with raw datasets, translation invariance and high computational efficiency. These advantages of CCNN make it particularly suitable for automated analyzing of the vibration data arisen from typical machine components such as the rolling element bearings which exhibit periodic phenomena with high non-stationary and strong noise contamination. Then, once an anomaly is detected, a further analysis technique to identify the fault is proposed using a multiresolution data analysis approach based on Double-Density Discrete Wavelet Transform (DD-DWT) which was grounded on over-sampled filter banks with smooth tight frames. This makes it nearly shift-invariant which is important for extracting non-stationary periodical peaks. Also, in order to denoise and enhance the diagnostic features, a novel level-dependant adaptive thresholding method based on harmonic to signal ratio (HSR) is developed and implemented on the selected wavelet coefficients. This method has been developed to be a semi-automated (adaptive) approach to facilitate the process of fault diagnosis. The developed framework has been evaluated using both simulated and measured datasets from typical healthy and defective tapered roller bearings which are critical parts of all rotating machines. The results have demonstrated that the CCNN is a robust technique for early fault detection, and also showed that adaptive DD-DWT is a robust technique for diagnosing the faults induced to test bearings. The developed framework has achieved multi-objectives of high detection sensitivity, reliable diagnosis and minimized computing complexity