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Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.


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Journal ArticleDOI
TL;DR: This study develops a physics-based multidimensional spatial-transient-stage tensor model to describe the thermo optical flow pattern for evaluating the contact fatigue damage and indicates that the proposed methods are effective tool for gear inspection and fatigue evaluation.
Abstract: Condition monitoring (CM), fault diagnosis (FD), and nondestructive testing (NDT) are currently considered crucial means to increase the reliability and availability of wind turbines. Many research works have focused on CM and FD for different components of wind turbine. Gear is typically used in a wind turbine. There is insufficient space to locate the sensors for long-term monitoring of fatigue state of gear, thus, offline inspection using NDT in both manufacturing and maintenance processes are critically important. This paper proposes an inductive thermography method for gear inspection. The ability to track the properties variation in gear such as electrical conductivity, magnetic permeability, and thermal conductivity has promising potential for the evaluation of material state undertaken by contact fatigue. Conventional thermography characterization methods are built based on single physical field analysis such as heat conduction or in-plane eddy current field. This study develops a physics-based multidimensional spatial-transient-stage tensor model to describe the thermo optical flow pattern for evaluating the contact fatigue damage. A helical gear with different cycles of contact fatigue tests was investigated and the proposed method was verified. It indicates that the proposed methods are effective tool for gear inspection and fatigue evaluation, which is important for early warning and condition-based maintenance.

89 citations

Proceedings ArticleDOI
04 Dec 2007
TL;DR: In this article, a 30 kW test rig has been constructed, with features similar to a wind turbine drive train, to enable the development of the signal processing techniques necessary for this variable speed, high torque variation application.
Abstract: Offshore wind turbines, incorporating electrical generators and converters, operate in locations where accessibility can lead to long mean times to repair. Condition-based maintenance is therefore essential if cost-effective availability targets are to be reached. As yet the condition monitoring techniques appropriate for offshore wind turbines have not been resolved. Reliability studies have shown that the majority of failure modes in wind turbines are concentrated in drive train subassemblies, including the electrical generator and converter, and are heavily affected by wind conditions. A 30 kW test rig has been constructed, with features similar to a wind turbine drive train, to enable the development of the signal processing techniques necessary for this variable speed, high torque variation application. The test rig includes a low speed shaft, high speed shaft, gearbox and an electrical generator and can be driven by simulated wind conditions. The test rig can also be used to inform the selection of appropriate monitoring instrumentation for offshore wind turbines. A series of condition monitoring approaches have been investigated on this test rig, using measured torque, speed, shaft displacement and gearbox vibration to detect faults. By the use of appropriate signal processing techniques, changes to load conditions, properties of the gearbox and coil faults can be detected.

89 citations

Journal ArticleDOI
TL;DR: In this paper, a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network was developed.
Abstract: This study develops a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network. This study also discusses the effect of the sensor installations, selected features, and the bandwidth size of the features on the classification rate. To collect the vibration signals necessary for training the system model and verifying the system, an experiment was implemented on a micro-milling research platform along with a 700 μm diameter micro-end mill and a SK2 workpiece. A three-axis accelerometer was installed on a sensor plate attached to the spindle housing to collect vibration signals in three directions during cutting. The frequency domain features representing changes in tool wear were selected based on the class mean scatter criteria after transforming signals from the time domain to the frequency domain by fast Fourier transform. Using the appropriate vibration features, this study develops and tests a backpropagation neural network classifier. Results show that proper feature extraction for classification provides a better solution than applying all spectral features into the classifier. Selecting five features for classification provides a better classification rate than the case with four and three features along with the 30 Hz bandwidth size of the spectral feature. Moreover, combining the signals for tool condition from both direction signals provides a better classification rate than determining the tool condition using a one-direction single sensor.

88 citations

Journal ArticleDOI
TL;DR: It is concluded that ANN-based fault diagnostic method is of great potential for future use and can also serve as an instant trend detector which greatly improves the current smoothing methods in trend detection.
Abstract: Application of artificial neural network (ANN)-based method to perform engine condition monitoring and fault diagnosis is evaluated. Back-propagation, feedforward neural nets are employed for constructing engine diagnostic networks. Noise-contained training and testing data are generated using an influence coefficient matrix and the data scatters. The results indicate that under high-level noise conditions ANN fault diagnosis can only achieve a 50-60 percent success rate. For situations where sensor scatters are comparable to those of the normal engine operation, the success rates for both four-input and eight-input ANN diagnoses achieve high scores which satisfy the minimum 90 percent requirement. It is surprising to find that the success rate of the four-input diagnosis is almost as good as that of the eight-input. Although the ANN-based method possesses certain capability in resisting the influence of input noise, it is found that a preprocessor that can perform sensor data validation is of paramount importance. Autoassociative neural network (AANN) is introduced to reduce the noise level contained. It is shown that the noise can be greatly filtered to result in a higher success rate of diagnosis. This AANN data validation preprocessor can also serve as an instant trend detector which greatly improves the current smoothing methods in trend detection. It is concluded that ANN-based fault diagnostic method is of great potential for future use. However, further investigations using actual engine data have to be done to validate the present findings.

88 citations

Journal ArticleDOI
TL;DR: The test results demonstrate that the novel neuro-fuzzy system, because of its adaptability and robustness, significantly improves the diagnostic accuracy, and outperforms other related classifiers, which adopt different types of rule weights and employ different training algorithms.
Abstract: The detection of the onset of damage in gear systems is of great importance to industry. In this paper, a new neuro-fuzzy diagnostic system is developed, whereby the strengths of three robust signal processing techniques are integrated. The adopted techniques are: the continuous wavelet transform (amplitude) and beta kurtosis based on the overall residual signal, and the phase modulation by employing the signal average. Three reference functions are proposed as post-processing techniques to enhance the feature characteristics in a way that increases the accuracy of fault detection. Monitoring indexes are derived to facilitate the automatic diagnoses. A constrained-gradient-reliability algorithm is developed to train the fuzzy membership function parameters and rule weights, while the required fuzzy completeness is retained. The system output is set to different monitoring levels by using an optimization procedure to facilitate the decision-making process. The test results demonstrate that the novel neuro-fuzzy system, because of its adaptability and robustness, significantly improves the diagnostic accuracy. It outperforms other related classifiers, such as those based on fuzzy logic and neuro-fuzzy schemes, which adopt different types of rule weights and employ different training algorithms.

88 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023164
2022413
2021798
2020927
2019936
2018906