scholarly journals Online Bearing Clearance Monitoring Based on an Accurate Vibration Analysis

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 389 ◽  
Author(s):  
Jianguo Wang ◽  
Minmin Xu ◽  
Chao Zhang ◽  
Baoshan Huang ◽  
Fengshou Gu
Keyword(s):  
Load Distribution ◽  
Lead Time ◽  
Cost Effective ◽  
Local Defect ◽  
Geometric Errors ◽  
Distribution Method ◽  
Outer Race ◽  
Bearing Clearance ◽  
Optimal Maintenance ◽  
Local Defects

Accurate diagnosis of incipient faults in wind turbine (WT) assets will provide sufficient lead time to apply an optimal maintenance for the expensive WT assets which often are located in a remote and harsh environment and their maintenance usually needs heavy equipment and highly skilled engineers. This paper presents an online bearing clearance monitoring approach to diagnose the change of bearing clearance, providing an early and interpretable indication of bearing health conditions. A novel dynamic load distribution method is developed to efficiently gain the general characteristics of vibration response of bearings without local defects but with small geometric errors. It shows that the ball pass frequency of outer race (BPFO) is the primary exciting source due to biased load distribution relating to bearing clearance. The geometric errors, including various orders of runouts on different bearing parts, can be the secondary excitation source. Both sources lead to compound modulation responses with very low amplitudes, being more than 20 dB lower than that of a small local defect on raceways and often buried by background noise. Then, Modulation Signal Bispectrum (MSB) is identified to purify the noisy signal and Gini index is introduced to represent the peakness of MSB results, thereby an interpretable indicator bounded between 0 and 1 is established to show bearing clearance status. Datasets from both a dedicated bearing test and a run-to-failure gearbox test are employed to verify the performance and reliability of the proposed approach. Results show that the proposed method is capable to indicate a change of about 20 µm in bearing clearance online, which provides a significantly long lead time compared to the diagnosis method that focuses only on local defects. Therefore, this method provides a big opportunity to implement more cost-effective maintenance works or carry out timely remedial actions to prolong the lifespan of bearings. Obviously, it is applicable to not only WT assets, but also most rotating machines.

An Experimental Approach in Defect Detection of a Single Row Ball Bearing Using Noise Generation Signal

2019 ◽  
Author(s):  
Aref Afsharfard ◽  
Seyed Hamid Reza Sanei
Keyword(s):  
Ball Bearing ◽  
Experimental Testing ◽  
Local Defect ◽  
Noise Generation ◽  
Frequency Spectra ◽  
Single Row ◽  
Outer Race ◽  
Bearing Defects ◽  
Cost Efficient ◽  
Local Defects

Abstract Bearings are critical mechanical components that are used in rotary machinery. Timely detection of defects in such components can prevent catastrophic failure. Noise is generated during the rotation of bearings even without the presence of defects due to finite number of rotating elements to carry the load. Such noise is associated with the change in effective stiffness during rotation, however, a sharp spike is observed in the noise level with presence of local defects. This study uses the noise generation aspect of roller bearings to identify local defect in a single row ball bearing with outer race stationary under radial load. Experimental testing is conducted on two identical bearings. The defective bearing is selected from a diesel engine subjected to 20 years of service. Dissecting the defective bearing revealed pitting and spalling of the inner race and balls, the most two common bearing defects. Both time and frequency analysis of sound pressure generated by the bearings were performed. The results show that there is a clear distinction in the time and frequency spectra between healthy and defective bearings. Findings of this study revealed that using a simple cost efficient in-house experimental setup, local defects can be readily detected.

scholarly journals Bayesian maintenance decision optimisation based on computing the information value from condition inspections

2021 ◽  
pp. 1748006X2097812
Author(s):  
Guang Zou ◽  
Kian Banisoleiman ◽  
Arturo González
Keyword(s):  
Decision Making ◽  
Structural Integrity ◽  
Fatigue Cracking ◽  
Cost Effective ◽  
Offshore Structures ◽  
Information Value ◽  
Bayesian Decision ◽  
Maintenance Strategies ◽  
Optimal Maintenance ◽  
Maintenance Decision

A challenge in marine and offshore engineering is structural integrity management (SIM) of assets such as ships, offshore structures, mooring systems, etc. Due to harsh marine environments, fatigue cracking and corrosion present persistent threats to structural integrity. SIM for such assets is complicated because of a very large number of rewelded plates and joints, for which condition inspections and maintenance are difficult and expensive tasks. Marine SIM needs to take into account uncertainty in material properties, loading characteristics, fatigue models, detection capacities of inspection methods, etc. Optimising inspection and maintenance strategies under uncertainty is therefore vital for effective SIM and cost reductions. This paper proposes a value of information (VoI) computation and Bayesian decision optimisation (BDO) approach to optimal maintenance planning of typical fatigue-prone structural systems under uncertainty. It is shown that the approach can yield optimal maintenance strategies reliably in various maintenance decision making problems or contexts, which are characterized by different cost ratios. It is also shown that there are decision making contexts where inspection information doesn’t add value, and condition based maintenance (CBM) is not cost-effective. The CBM strategy is optimal only in the decision making contexts where VoI > 0. The proposed approach overcomes the limitation of CBM strategy and highlights the importance of VoI computation (to confirm VoI > 0) before adopting inspections and CBM.

Real-Time Process Quality Monitoring Using Wireless Sensor-Embedded Rotary Tools for Fastening Operations

2007 ◽  
Author(s):  
Deepak T. Mohan ◽  
Jeffrey Birt ◽  
Can Saygin ◽  
Jaganathan Sarangapani
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Value Added ◽  
Cost Effective ◽  
Base Station ◽  
Embedded Sensors ◽  
Optical Encoder ◽  
Effective Manner ◽  
Rotary Type

Fastening operations are extensively used in the aerospace industry and constitute for more than a quarter of the total cost. Inspection of fasteners is another factor that adds cost and complexity to the overall process. Inspection is usually carried out on a sampling-basis as a stand-alone process after the fastening process is completed. Lack of capability to inspect all fasteners in a cost effective manner and the need to remove non-value added activities, such as inspection by itself, in order to reduce the manufacturing lead time have been the motivation behind this study. This paper presents a novel diagnostics scheme based on Mahalanobis-Taguchi System (MTS) for monitoring the quality of rotary-type fastening operations in real-time. This approach encompasses (1) integrating a torque sensor, a pressure sensor, and an optical encoder on a hand-held rotary-type fastening tool; (2) obtaining process parameters via the embedded sensors and generating process signatures in real-time; and (3) detecting anomalies on the tool using a wireless mote that communicates the decision with a base station. The anomalies investigated in this study are the grip length variations as under grip and normal grip, and presence of re-used fasteners. The proposed scheme has been implemented on prototype rotary tool for bolt-nut type of fasteners and tested under a variety of experimental settings. The experimental results have shown that the proposed approach is successful, with an accuracy of over 95% in detecting grip lengths of fasteners in real-time during the process.

Low Order Static Load Distribution Model for Ball Screw Mechanisms Including Effects of Lateral Deformation and Geometric Errors

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Bo Lin ◽  
Chinedum E. Okwudire ◽  
Jason S. Wou
Keyword(s):  
Load Distribution ◽  
Static Load ◽  
High Order ◽  
Contact Model ◽  
Ball Screw ◽  
Distribution Model ◽  
Computational Time ◽  
Geometric Errors ◽  
Lateral Deformation ◽  
Low Order

Accurate modeling of static load distribution of balls is very useful for proper design and sizing of ball screw mechanisms (BSMs); it is also a starting point in modeling the dynamics, e.g., friction behavior, of BSMs. Often, it is preferable to determine load distribution using low order models, as opposed to computationally unwieldy high order finite element (FE) models. However, existing low order static load distribution models for BSMs are inaccurate because they ignore the lateral (bending) deformations of screw/nut and do not adequately consider geometric errors, both of which significantly influence load distribution. This paper presents a low order static load distribution model for BSMs that incorporates lateral deformation and geometric error effects. The ball and groove surfaces of BSMs, including geometric errors, are described mathematically and used to establish a ball-to-groove contact model based on Hertzian contact theory. Effects of axial, torsional, and lateral deformations are incorporated into the contact model by representing the nut as a rigid body and the screw as beam FEs connected by a newly derived ball stiffness matrix which considers geometric errors. Benchmarked against a high order FE model in case studies, the proposed model is shown to be accurate in predicting static load distribution, while requiring much less computational time. Its ease-of-use and versatility for evaluating effects of sundry geometric errors, e.g., pitch errors and ball diameter variation, on static load distribution are also demonstrated. It is thus suitable for parametric studies and optimal design of BSMs.

scholarly journals Self-Organizing Map (SOM) in Wind Speed Forecasting: A New Approach in Computational Intelligence (CI) Forecasting Methods

2012 ◽  
Author(s):  
Mohammad Amin Esmaeili ◽  
Janet Twomey
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Cost Effective ◽  
Self Organizing Map ◽  
Electricity Grid ◽  
Average Maximum ◽  
Critical Issues ◽  
Electricity Grids ◽  
Optimal Maintenance ◽  
Self Organizing

While wind energy has been reported as the fastest growing among different sources of renewable energy, two critical issues are how to make wind energy cost effective and how to integrate it into electricity grids properly. The ability to predict power generated by wind not only allows the most effective integration of wind power into electricity grid but also makes it possible to have an optimal maintenance scheduling that can reduce cost significantly. This research investigates the practical use of Self Organizing Map (SOM) as a special type of neural network based forecasting method. In this paper, forecasting the average, maximum and minimum of one-day-ahead wind speed based on the past wind speed states of the previous 24 hours is the objective.

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