New Focus on Gearbox Condition Monitoring for Failure Prevention Technology

2013 ◽  
Vol 588 ◽  
pp. 184-191 ◽  
Author(s):  
Walter Bartelmus
Keyword(s):  
Condition Monitoring ◽  
Degradation Process ◽  
Dust Particles ◽  
Oil Contamination ◽  
Failure Prevention ◽  
Operation Conditions ◽  
Frictional Wear ◽  
Rolling Elements ◽  
Oil Purification ◽  
Further Development

Condition monitoring is a tool for detection of faults and failure prevention. Fault andfailure are regarded as inevitable during the machine operation as the process of wear and theprocess of degradation. The question is, if one can influence the wear and degradation process,using condition monitoring. The paper will present technology which demonstrates that the use ofthe proper method can influence the wear and machine degradation process, using proper conditionmonitoring techniques and knowing scenarios of wear and degradation process. In the discussionpresented in the paper as a prerequisite has been taken that machinery works in severe dustyenvironment and varying operation conditions. It has been pointed that degradation process is notjust simply development of one fault. Most research for developing technology for conditionmonitoring is concentrated on one fault development. If one considers condition monitoring for acrack and brakeage of a tooth in gearbox, one should take in consideration that tooth crack andbrakeage is the result of several events, like rolling elements bearing frictional wear, which causesecondary misalignment of shaft and gears. The frictional wear is caused by dust particles whichget into oil from the environment in which a gearbox is operating. To avoid an influence ofcontaminated oil, contamination proactive technology should be used for the assessment of thedegree of contamination and the decision on oil purification or change should be taken. The wholeprocess connected with a gearbox condition change (wear and degradation process) shall bedescribed in the paper. The oil purification or its replacement extends the live of gearboxes butlong live of a gearbox, even with very little contamination causes some frictional wear of bearingsand finally secondary misalignment. To avoid further development of degradation process propertechnology should be used. There is a need to measure the degree of misalignment and makedecision on bearings replacement, in order to avoid further gearbox degradation, like teeth scuffingwhich may leads to crack initiation.

scholarly journals Surface Changes of Selected Hardwoods Due to Weather Conditions

Forests ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 557 ◽  
Author(s):  
Ivan Kubovský ◽  
Eliška Oberhofnerová ◽  
František Kačík ◽  
Miloš Pánek
Keyword(s):  
Laser Scanning ◽  
Weather Conditions ◽  
Degradation Process ◽  
Climatic Conditions ◽  
Laser Scanning Microscopy ◽  
Dust Particles ◽  
Confocal Laser ◽  
Chemical Changes ◽  
Colour Changes ◽  
Surface Changes

The study is focused on the surface changes of five hardwoods (oak, black locust, poplar, alder and maple) that were exposed to natural weathering for 24 months in the climatic conditions of Central Europe. Colour, roughness, visual and chemical changes of exposed surface structures were examined. The lowest total colour changes (ΔE*) were found for oak (23.77), the highest being recorded for maple (34.19). Roughness differences after 24-month exposure (ΔRa) showed minimal changes in poplar wood (9.41); the highest changes in roughness were found on the surface of alder (22.18). The presence of mould and blue stains was found on the surface of maple, alder and poplar. Chemical changes were characterized by lignin and hemicelluloses degradation. Decreases of both methoxy and carbonyl groups, cleavage of bonds in lignin and hemicelluloses, oxidation reaction and formation of new chromophores were observed. In the initial phases of the degradation process, the discoloration was related to chemical changes; in the longer period, the greying due to settling of dust particles and action of mould influenced the wood colour. The data were confirmed by confocal laser scanning microscopy. The obtained results revealed degradation processes of tested hardwood surfaces exposed to external environmental factors.

Multi-Zone Proportional Hazard Model for a Multi-Stage Degradation Process

2013 ◽  
Author(s):  
Lin Li ◽  
Zeyi Sun ◽  
Xinwei Xu ◽  
Kaifu Zhang
Keyword(s):  
Decision Making ◽  
Condition Monitoring ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Monitoring Data ◽  
Status Change ◽  
Multi Stage ◽  
And Performance

Conditional-based maintenance (CBM) decision-making is of high interests in recent years due to its better performance on cost efficiency compared to other traditional policies. One of the most respected methods based on condition-monitoring data for maintenance decision-making is Proportional Hazards Model (PHM). It utilizes condition-monitoring data as covariates and identifies their effects on the lifetime of a component. Conventional modeling process of PHM only treats the degradation process as a whole lifecycle. In this paper, the PHM is advanced to describe a multi-zone degradation system considering the fact that the lifecycle of a machine can be divided into several different degradation stages. The methods to estimate reliability and performance prognostics are developed based on the proposed multi-zone PHM to predict the remaining time that the machine stays at the current stage before transferring into the next stage and the remaining useful life (RUL). The results illustrate that the multi-zone PHM effectively monitors the equipment status change and leads to a more accurate RUL prediction compared with traditional PHM.

scholarly journals Condition Monitoring Technologies for Synthetic Fiber Ropes - a Review

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Espen Oland ◽  
Rune Schlanbusch ◽  
Shaun Falconer
Keyword(s):  
Condition Monitoring ◽  
State Of The Art ◽  
Degradation Process ◽  
Synthetic Fiber ◽  
Physical Parameters ◽  
The Subject ◽  
Condition Monitoring System ◽  
Future Direction ◽  
Monitoring Technologies ◽  
Insight Into

This paper presents a review of different condition monitoring technologies for fiber ropes. Specifically, it presents an overview of the articles and patents on the subject, ranging from the early 70’s up until today with the state of the art. Experimental results are also included and discussed in a conditionmonitoring context,where failuremechanisms and changes in physical parameters give improved insight into the degradation process of fiber ropes. From this review, it is found that automatic width measurement has received surprisingly little attention, and might be a future direction for the development of a continuous condition monitoring system for synthetic fiber ropes.

Force Analysis of Double Pitch Point Cycloid Drive with Intermediate Rolling Elements and Free Retainer

2015 ◽  
Vol 756 ◽  
pp. 29-34
Author(s):  
E.A. Efremenkov ◽  
E.E. Kobza ◽  
S.K. Efremenkova
Keyword(s):  
Performance Improvement ◽  
Wedge Angle ◽  
Force Distribution ◽  
Force Analysis ◽  
Angle Variation ◽  
Rolling Elements ◽  
Further Development

This paper illustrates the analysis of wedge angle influence on force distribution in the meshing of double pitch point cycloid drive in comparison with single pitch point cycloid drive. Double pitch point cycloid drive may provide smoother performance of transmission at starting period in consequence of wedge angle variation capabilities. Matching initial parameters it is possible to modify the wedge angle and achieve its effective value. The influence of various combinations of initial parameters on the wedge angle and retainer force was studied and presented on diagrams. Some recommendations in designing related to performance improvement are given. The obtained results can be used for further development of better designs of cycloid drives.

scholarly journals Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
A. Romero ◽  
Y. Lage ◽  
S. Soua ◽  
B. Wang ◽  
T.-H. Gan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Health Assessment ◽  
Field Trials ◽  
Health Condition ◽  
Normal Operation ◽  
Wind Turbine Gearbox ◽  
Operation Conditions

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.

Modeling Multi-State Equipment Degradation with Non-Homogeneous Continuous-Time Hidden Semi-Markov Process

2013 ◽  
pp. 151-181 ◽  
Author(s):  
Ramin Moghaddass ◽  
Ming J Zuo ◽  
Xiaomin Zhao
Keyword(s):  
Condition Monitoring ◽  
Continuous Time ◽  
Degradation Process ◽  
Health Condition ◽  
Health Conditions ◽  
Mechanical Equipment ◽  
Technical Problems ◽  
Semi Markov Process ◽  
Overall Performance ◽  
Actual Health

The multi-state reliability analysis has received great attention recently in the domain of reliability and maintenance, specifically for mechanical equipment operating under stress, load, and fatigue conditions. The overall performance of this type of mechanical equipment deteriorates over time, which may result in multi-state health conditions. This deterioration can be represented by a continuous-time degradation process with multiple discrete states. In reality, due to technical problems, directly observing the actual health condition of the equipment may not be possible. In such cases, condition monitoring information may be useful to estimate the actual health condition of the equipment. In this chapter, the authors describe the application of a general stochastic process to multi-state equipment modeling. Also, an unsupervised learning method is presented to estimate the parameters of this stochastic model from condition monitoring data.

scholarly journals Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1474 ◽  
Author(s):  
Francesco Castellani ◽  
Luigi Garibaldi ◽  
Alessandro Paolo Daga ◽  
Davide Astolfi ◽  
Francesco Natili
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Statistical Tests ◽  
Novelty Detection ◽  
General Idea ◽  
Data Set ◽  
Vibration Measurements ◽  
Bearing Fault ◽  
Operation Conditions

Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring.

scholarly journals A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Omar AlShorman ◽  
Muhammad Irfan ◽  
Nordin Saad ◽  
D. Zhen ◽  
Noman Haider ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Induction Motor ◽  
Condition Monitoring ◽  
Fault Detection And Diagnosis ◽  
Time Frequency ◽  
Rolling Elements ◽  
Detection And Diagnosis ◽  
Signal Image ◽  
And Performance ◽  
Main Component

The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted.

scholarly journals Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7129
Author(s):  
Ana Rita Nunes ◽  
Hugo Morais ◽  
Alberto Sardinha
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Predictive Maintenance ◽  
Machine Learning Techniques ◽  
Learning Mechanisms ◽  
Learning Stage ◽  
Operation Conditions ◽  
Turbine Generators ◽  
Wind Turbine Generators ◽  
Learning Techniques

The main goal of this paper is to review and evaluate how we can take advantage of state-of-the-art machine learning techniques and apply them in wind energy operation conditions monitoring and fault diagnosis, boosting wind turbines’ availability. To accomplish this, we focus our work on analysing the current techniques in predictive maintenance, which are aimed at acting before a major failure occurs using condition monitoring. In particular, we start framing the predictive maintenance problem as an ML problem to detect patterns that indicate a fault on turbine generators. Then, we extend the problem to detect future faults. Therefore, this review will consist of analysing techniques to tackle the challenges of each machine learning stage, such as data pre-processing, feature engineering, and the selection of the best-suited model. By using specific evaluation metrics, the expected final result of using these techniques will be an improvement in the early prediction of a future fault. This improvement will have an increase in the availability of the turbine, and therefore in energy production.

scholarly journals Large Engine Hybrid Ceramic Bearings

1994 ◽  
Author(s):  
Mark A. Rhoads ◽  
Manohar Bashyam ◽  
William J. Crecelius
Keyword(s):  
Condition Monitoring ◽  
High Speed ◽  
Shock Loading ◽  
Monitoring Device ◽  
Loading Conditions ◽  
Large Engine ◽  
Bearing Condition Monitoring ◽  
Rolling Elements ◽  
Hybrid Bearing ◽  
Full Scale Tests

General Electric Aircraft Engines under contract from the Advanced Research Projects Agency (ARPA), has demonstrated the ability of ceramic rolling elements to withstand shock loading conditions experienced during race spalling, has performed a series of full scale tests directed at showing the thermal benefit of large hybrid bearings at speeds up to 3.0 MDN, and has developed a condition monitoring device that detects both ceramic and metallic bearing debris. The details of the three primary tasks are presented in this paper: Task 1 involves the testing of a hybrid bearing operating in severe shock loading conditions, with comparisons to an all steel bearing. Task 2 involves back-to-back comparison of an all-steel high speed bearing to a hybrid bearing of the same geometry and to a hybrid bearing of tighter race curvatures, showing differences between outer ring temperatures of all-steel and hybrid bearings. Task 3 deals with the bench testing of a new ultrasonic bearing condition monitoring device, designed to collect and detect both ceramic or metallic debris.

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