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This work aims to demonstrate the vibration suppression of the rotor system with localized defects on bearing using an integral squeeze film damper (ISFD).

Experiments were carried out to study the vibration characteristics of the rotor system with ISFD mounted on fault deep groove ball bearings. Three fault bearings including bearing with outer race defect, inner race defect and ball defect have been used in this paper. The results were compared by use of vibration acceleration level, continuous wavelet transform and envelope spectrum.

It was found that ISFD shows excellent damping and vibration attenuation characteristics of the rotor system with defective bearing. The fault bearing rotor system with external ISFD considerably reduces the vibration energy and amplitude compared with the system without ISFD.

There is a dearth of experimental research pertaining to vibration characteristics of rotor system support by defective bearings with ISFD. Besides, the test provides evidence for the application of ISFD in vibration control of the rotor system with incipient defects on bearing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2020-0144/

Combined defects in ball bearings may be caused during the use or manufacturing process, which can significantly affect their vibration characteristics. The previous defect models in the literature can only describe single defects such as the surface waviness and localized defect. This paper aims to propose an in-depth understanding of radial vibrations of a ball bearing with the combined defect.

A dynamic model for a ball bearing with the combined defect including the surface waviness and localized defect on its races is proposed. The effects of the combined defect sizes on the radial bearing vibrations are investigated. The results from the proposed model considering the combined defect are compared with the available results from the previous methods considering the single defects.

The acceleration amplitude is significantly affected by the surface waviness, localized defect and the combined defect on its races. The effect of the combined defect on the acceleration amplitude is larger than that of the single defect. The amplitude and peak frequency of the spectrum of acceleration for the combined defect increases with the defect sizes. The RMS value of the accelerations for the combined defect increases with the combined defect sizes.

Consequently, the proposed model can predict more accurate and in-depth understanding of the radial vibrations caused by the combined defect in the ball bearing.

The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis. The purpose of this work is to provide an approach for maintenance engineers for online fault diagnosis through the development of a machine condition‐monitoring system.

A detailed review of previous work carried out by several researchers and maintenance engineers in the area of machine‐fault signature‐analysis is performed. A hybrid expert system is developed using ANN, Fuzzy Logic and Wavelet Transform. A Knowledge Base (KB) is created with the help of fuzzy membership function. The triangular membership function is used for the generation of the knowledge base. The fuzzy‐BP approach is used successfully by using LR‐type fuzzy numbers of wavelet‐packet decomposition features.

The development of a hybrid system, with the use of LR‐type fuzzy numbers, ANN, Wavelets decomposition, and fuzzy logic is found. Results show that this approach can successfully diagnose the bearing condition and that accuracy is good compared with conventionally EBPNN‐based fault diagnosis.

The work presents a laboratory investigation carried out through an experimental set‐up for the study of mechanical faults, mainly related to the rolling element bearings.

The main contribution of the work has been the development of an expert system, which identifies the fault accurately online. The approaches can now be extended to the development of a fault diagnostics system for other mechanical faults such as gear fault, coupling fault, misalignment, looseness, and unbalance, etc.

The main objective of the paper is to explore the theoretical correlation of base oil viscosity in grease and to study the effect of grease grade on mechanical vibrations associated with the damaged rolling bearings.

For theoretical purposes, formulation theory of dimensional analysis was implemented. Experiments were then performed on the test bearings lubricated with three different types of greases, namely, SKF LGHP2, SKF LGMT3 and SKF LGWA2.

The numerical results obtained from the theoretical model along with the results of experiments show that the vibration amplitudes of the defective bearings come down to a lower level when it is lubricated with the grease of a higher base oil viscosity.

The promising results from the theoretical model make it usable for the practical rotating machineries applying a variety of the rolling bearings. Consequently, if the bearing is not severely damaged, its performance can be increased by lubricating it with thicker grease.

Despite many significant contributions in the field to detect the presence of defects, not many studies have been performed that relate the lubrication condition of the rolling bearings with the vibration response, because around 50-75% of the bearing failures are attributed to be lubrication related. Hence, there is need to develop a mathematical model that can correlate the vibration severity of the bearings with viscosity of the lubricant oil in the greases along with other design and operating parameters.

The usefulness of probability density and cross‐correlation of the vibration acceleration signal of rolling element bearings has been investigated. These measurements have been performed on ball bearings with and without simulated defects in their races after mounting them on a test rig. The measurement results show that both probability density and cross‐correlation of the bearings′ vibration signal can be used to detect defects in them.

The purpose of this paper is to classify different conditions of the rolling element bearing (REB) using vibration signals acquired from a customized bearing test rig.

An effort has been made to develop health index (HI) based on singular values of the statistical features to classify different conditions of the REB. The vibration signals from the normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under variable load and speed conditions. These signals were subjected to “modified kurtosis hybrid thresholding rule” (MKHTR)-based denoising. The denoised signals were decomposed using discrete wavelet transform. A total of 17 statistical features have been extracted from the wavelet coefficients of the decomposed signal.

Singular values of the statistical features can be effectively used for REB classification.

REB are critical components of rotary machinery right across the industrial sectors. It is a well-known fact that critical bearing failures causes major breakdowns resulting in untold and most expensive downtimes that should be avoided at all costs. Hence, intelligently based bearing failure diagnosis and prognosis should be an integral part of the asset maintenance and management activity in any industry using rotary machines.

It is found that singular values of the statistical features exhibit a constant value and accordingly can be assigned to each type of bearing fault and can be used for fault characterization in practical applications. The effectiveness of this index has been established by applying this to data from Case Western Reserve University data base which is a standard bench mark data for this application. HIs minimizes the computation time when compared to fault diagnosis using soft computing techniques.

The purpose of this paper is to test the applicability and the performance of an approach called logical analysis of data (LAD) on the detection of faults in rotating machinery using vibration signals.

LAD is a supervised learning data mining technique that relies on finding patterns in a binary database to generate decision functions. The hypothesis is that a LAD‐based decision model can be used as an effective tool for automatic detection of faults in rolling element bearings. A novel Multiple Integer Linear Programming approach is used to generate patterns for the LAD decision model. Frequency and time‐based features are extracted from rotor bearing vibration signals and are pre‐processed to be suitable for use with LAD.

The results show good classification accuracy with both time and frequency features.

The diagnostic tool implemented in the form of software in a production or operations maintenance environment can be very helpful to maintenance experts as it reveals the patterns that lead to the diagnosis in interpretable terms which facilitates efforts to understand the reasons behind the components' failure.

The proposed modifications to the LAD‐based decision model which is being tested for the first time in the field of fault detection in rotating machinery lead to improved accuracy results in addition to the added value of result interpretability due to this distinctive property of LAD.

The purpose of this paper is to extract features from vibration signals measured from induction motors subjected to accelerated aging of bearings by fluting tests.

Aging tests were performed according to IEEE test procedures. The data acquisition involved the measurement of vibration signals using accelerometers that were installed on the bearings and on the motor casing. In this application, only two accelerometers, which were placed near the process end of the motor bearing, are used for data analysis and feature extraction studies. After the data collection, information from the two sensors was combined using simple sensor fusion method under the linearity conditions, and then spectral analysis and time‐scale analysis were performed. The fused vibration signal is decomposed into several scales using continuous wavelet transform (CWT) and its first scale is used to indicate the bearing degradation.

Bearing damage characterization was determined between 2‐4 kHz and some specific frequencies were calculated as harmonics of the bearing characteristic frequencies.

The bearing damage characteristics used in this study is occurred by the experimental study. In terms of the methodology, the use of the CWT shows the fault characteristics from the initial case.

The experimental study and data acquisition are based on the accelerated aging of the motor bearings. Hence, the real aging is represented by the accelerated one. But, this situation reflects same properties of the aging occurred in industrial environments. The methodology is also applicable to the hardware application.

There are two important aspects of this research: the experimental study and the application of CWT to get the potential defects, which will appear as a failure in future, from the healthy case of the motor bearings.

The aim of this paper is to present a theoretical model to simulate the dynamic behavior of a spur gear, taking into account its ball bearings defects.

The proposed model is based on the implicit Newmark‐β with Newton‐Raphson numerical integration technique in order to analyze the impact of the worn bearings on the non linear dynamic behavior of one stage spur gear transmission system.

The dynamic behavior of spur gear is studied taking into account ball bearings defects thanks to the proposed model.

A new numerical model is proposed to simulate the dynamic behavior of rotating spur gear system taking into account both waviness and backlash defects.

In rotary machines, the bearing failure is one of the major causes of the breakdown of machinery. The bearing degradation monitoring is a great anxiety for the prevention of bearing failures. This paper aims to present a combination of the stationary wavelet decomposition and extra-trees regression (ETR) for the evaluation of bearing degradation.

The higher order cumulants features are extracted from the bearing vibration signals by using the stationary wavelet decomposition (stationary wavelet transform [SWT]). The extracted features are then subjected to the ETR for obtaining normal and failure state. A dominance level curve build using the dissimilarity data of test object and retained as health degradation indicator for the evaluation of bearing health.

Experiment conducts to verify and assess the effectiveness of ETR for the evaluation of performance of bearing degradation. To justify the preeminence of recommended approach, it is compared with the performance of random forest regression and multi-layer perceptron regression.

The experimental results indicated that the presently adopted method shows better performance for detecting the degradation more accurately at early stage. Furthermore, the diagnostics and prognostics have been getting much attention in the field of vibration, and it plays a significant role to avoid accidents.