Towards zero-configuration condition monitoring based on dictionary learning

Martin-del-Campo, Sergio; Sandin, Fredrik

doi:10.1109/eusipco.2015.7362595

Cited by 9 publications

(7 citation statements)

References 14 publications

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“…The methodology includes a sparse regularization mechanism that reduces the influence of noise and some of the redundancy that is typically present in raw sensor signals. Here, the hypothesis is that the same general approach can be used to characterize and analyze the signals generated by a rotating machine [21].…”

Section: Introductionmentioning

confidence: 99%

Dictionary Learning Approach to Monitoring of Wind Turbine Drivetrain Bearings

Martin-del-Campo¹,

Sandin²,

Strömbergsson³

2020

IJCIS

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Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development, and a large number of aging wind turbines. In particular, predictive maintenance planning requires the early detection of faults with few false positives. Achieving this type of detection is a challenging problem due to the complex and weak signatures of some faults, particularly the faults that occur on the gearbox bearings of a turbine drivetrain. The results of former studies addressing condition-monitoring tasks using dictionary learning indicate that unsupervised feature learning is useful for diagnosis and anomaly detection purposes. However, these studies are based on small sets of labeled data from test rigs operating under controlled conditions that focus on classification tasks, which are useful for quantitative method comparisons but gives little insight into how useful these approaches are in practice or how can be used by existing condition-monitoring systems. Here, we investigate an unsupervised dictionary learning method for condition monitoring using vibration data recorded over 46 months under typical industrial operations. Thus, we contribute real-world industrial vibration data that are made publicly available and novel test results. In this study, dictionaries are learned from gearbox vibrations in six different turbines, and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur. We perform the experiment using two different sparse coding algorithms to investigate if the algorithm selected affects the features of abnormal conditions. We propose a dictionary distance metric derived from the dictionary learning process as a condition indicator and find the time periods of abnormal dictionary adaptation starting six months before a drivetrain bearing replacement and one year before the resulting gearbox replacement. In addition, we investigate the distance between dictionaries learned from geographically close turbines of the same type under healthy conditions. We find that the features learned are similar and that a dictionary learned from one turbine can be useful for monitoring a similar turbine.

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Section: Introductionmentioning

confidence: 99%

Dictionary Learning Approach to Monitoring of Wind Turbine Drivetrain Bearings

Martin-del-Campo¹,

Sandin²,

Strömbergsson³

2020

IJCIS

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“…However, δ can be held constant and as such, the adaptation rate can be used to quantify sudden abnormal changes in the signal, for example, due to sudden faults in the machine. See also 47 where the rate of change of individual atoms after the introduction of a fault is investigated.…”

Section: Sparse Feature Learningmentioning

confidence: 99%

Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning

Martin-del-Campo

Sandin

Schnabel³

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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We analyze vibration signals from wind turbines with dictionary learning and investigate the relation between dictionary distances and faults occurring in a wind turbine output shaft rolling element bearing and gearbox under different data and compute constraints. Dictionary learning is an unsupervised machine learning method for signal processing, which permits learning a set of signal-specific features that have been used to monitor the condition of rotating machines, including wind turbines. Dictionary distance is one such feature, and its effectiveness depends on an adequate selection of the dictionary learning hyperparameters and the data availability, which typically is constrained in condition monitoring systems for remotely located wind farms. Here we evaluate the characteristics of the dictionary distance feature under healthy and faulty conditions of the wind turbines using different options for the selection of the pretrained dictionary, the sparsity of the signal model which determines the compute requirements, and the interval between data samples. Furthermore, we compare the dictionary distance feature to the typical time-domain features used in condition monitoring. We find that the dictionary distance based feature of a faulty wind turbine deviates by a factor of two or more from the population distribution several weeks before the gearbox bearing fault was reported, using a data sampling interval as long as 24 h and a model sparsity as low as 2.5%.

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“…The idea is that the adaptation rate can be used to quantify sudden abnormal changes in the signal, for example due to a fault in the system. See also [21] where the rate of change of individual atoms after the introduction of a fault is investigated. In principle, the dictionary distance β(Φ t , Φ t−δ ) could be normalized with the time step δ to obtain a finite difference approximation of the "dictionary derivative" with respect to time.…”

Section: Dictionary Distancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dictionary learning approach to monitoring of wind turbine drivetrain bearings

Martin-del-Campo¹,

Sandin²,

Strömbergsson³

2019

Preprint

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Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development and large number of aging wind turbines. In particular, predictive maintenance planning requires the early detection of faults with few false positives. Achieving this type of detection is a challenging problem due to the complex and weak signatures of some faults, particularly the faults that occur in some of the drivetrain bearings. Here, we investigate recently proposed condition monitoring methods based on unsupervised dictionary learning using vibration data recorded over 46 months under typical industrial operations. Thus, we contribute novel test results and real-world data that are made publicly available. The results of former studies addressing condition-monitoring tasks using dictionary learning indicate that unsupervised feature learning is useful for diagnosis and anomaly detection purposes. However, these studies are based on small sets of labeled data from test rigs operating under controlled conditions that focus on classification tasks, which are useful for quantitative method comparisons but gives little insight into how useful these approaches are in practice. In this study, dictionaries are learned from gearbox vibrations in six different turbines, and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur. We perform the experiment using two different sparse coding algorithms to investigate if the algorithm selected affects the features of abnormal conditions. We calculate the dictionary distance between the initial and propagated dictionaries and find the time periods of abnormal dictionary adaptation starting six months before a drivetrain bearing replacement and one year before the resulting gearbox replacement. In addition, we investigate the distance between dictionaries learned from geographically close turbines of the same type under healthy conditions. We find that the features learned are similar and that a dictionary learned from one turbine can be useful for monitoring a similar turbine.

show abstract

Towards zero-configuration condition monitoring based on dictionary learning

Cited by 9 publications

References 14 publications

Dictionary Learning Approach to Monitoring of Wind Turbine Drivetrain Bearings

Dictionary Learning Approach to Monitoring of Wind Turbine Drivetrain Bearings

Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning

Dictionary learning approach to monitoring of wind turbine drivetrain bearings

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