Analysis of an Angular Contact Ball Bearing Failure and Strategies for Failure Prevention

Murugesan, V.; Sreejith, P. S.; Sundaresan, P.; Ramasubramanian, V.

doi:10.1007/s11668-018-0441-5

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…Rolling-bearing faults represent up to 51% of all rotating mechanical problems, making them one of the main factors affecting the reliable and safe operation of mechanical systems [9,10]. The identification of the root cause of the bearing failure becomes very complex due to the combination of several factors, such as the misalignment of the ball bearing fitted in the main shaft of an aero engine, as described by Ejaz et al [11], or the misalignment in the angular contact ball bearing, as investigated by Murugesan et al [12]. Other examples of bearing failures can originate from undesirable electrical currents between the track surfaces for races [13] or improper assembly operation, such as the use of excessive tightening force, as reported by Hou et al [14].…”

Section: Introductionmentioning

confidence: 99%

Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure

Primera,

Fernández,

Cacereño

et al. 2024

Machines

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Roller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on the largest cylindrical roller bearings as the main component that causes equipment failure. The objective of this work is to make a prognosis of when the overall vibrations would reach the maximum level allowed (2.5 IPS pk), thus enabling planned replacements, and achieving the maximum possible useful life in operation, without incurring unscheduled corrective maintenance and unexpected plant shutdown. Wireless sensors were used to capture vibration data and the ARIMA (Auto-Regressive Integrated Moving Average) and Holt–Winters methods were applied to forecast vibration behavior in the short term. Finally, the results demonstrate that the Holt–Winters model outperforms the ARIMA model in precision, allowing a 3-month prognosis without exceeding the established vibration limit.

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Section: Introductionmentioning

confidence: 99%

Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure

Primera,

Fernández,

Cacereño

et al. 2024

Machines

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show abstract

“…The main faults on rolling bearings are on the inner and outer ring surfaces, raceway surface, rolling body surface, rolling body and raceway contact surface, cage and rolling body or seat ring surface, ring edge and roller end face, and inner and outer ring raceway surface and subsurface [ 15 , 16 , 17 ]. At the same time, different bearing elements have corresponding defect frequencies, inner ring defect frequency (BPFI), outer ring defect frequency (BFPO), ball bearing defect frequency (BSF), and cage defect frequency (FTF).…”

Section: Introductionmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

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As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.

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“…Since the Industrial Revolution in the 1860s, machinery has been related to all aspects of our lives, from national strategic equipment to daily travelling vehicles. Due to the long-term service, complex working conditions and harsh working environment of mechanical equipment, its parts are easy to be damaged during operation, which may lead to component failure 1 , 2 . In serious cases, it may even cause major safety accidents and economic losses 3 , 4 .…”

Section: Introductionmentioning

confidence: 99%

Research on an intelligent diagnosis method of mechanical faults for small sample data sets

Zhao

Shi

Tan

et al. 2022

Sci Rep

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The difficulty of feature extraction and the small sample size are two challenges in the field of mechanical fault diagnosis for a long time. Here we propose an intelligent mechanical fault diagnosis method for scenario with small sample datasets. This method can not only diagnose bearing faults but also gear faults, and has strong generalization performance. We use convolutional neural network to realize automatic feature extraction. Through sliding window scanning, one sample set is expanded to three sub-sample sets with different scales to meet the needs of deep learning training. Three convolutional networks are used to extract the features of the subsets respectively to ensure that their useful features are fully extracted. After feature extraction, the feature is reconstructed through feature splicing. Because of the unique advantages of SVM in dealing with small sample sets, we use SVM to classify the reconstructed features. We use the bearing data set collected by Case Western Reserve University in the United States, the bearing fault data set collected by Xi'an Jiaotong University in China, and the gearbox fault data collected by the University of Connecticut in the United States to conduct experiments. The experimental results show that the accuracy of training, validation and testing of the proposed method on the three data sets all reach 100%. This proves that our method can not only tackle the two challenges, but also has high fault diagnosis accuracy and strong generalization performance. It is hoped that our proposed method can contribute to the development of mechanical fault diagnosis.

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Analysis of an Angular Contact Ball Bearing Failure and Strategies for Failure Prevention

Cited by 12 publications

References 5 publications

Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure

Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Research on an intelligent diagnosis method of mechanical faults for small sample data sets

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