Data-driven imbalance and hard particle detection in rotating machinery using infrared thermal imaging

Janssens, Olivier; Loccufier, Mia; Walle, Rik Van de; Hoecke, Sofie Van

doi:10.1016/j.infrared.2017.02.009

Cited by 12 publications

(5 citation statements)

References 18 publications

(37 reference statements)

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“…For each health condition of gearbox, the temperature influence was considered, thereby, the IRT videos were recorded throughout the entire temperature-rising process using the thermal camera. The experiment is terminated when the gearbox comes into a steady stage [29,40]. To determine whether the temperature of gearbox reaches a stable condition, a circle around the gear meshing area is added as a reference, in which the high-, average-, and low-temperature values can be observed, as shown in Figure 6a.…”

Section: Experimental Irt Images Acquisitionmentioning

confidence: 99%

“…Janssens et al combined the three features extracted from IRT images with a SVM to conduct the fault diagnosis of rolling bearings [28]. Meanwhile, the Gini coefficient and machine learning methods are applied for early fault detection of rotating machinery using IRT images by Janssens et al [29]. From the above literature review, we find that IRT images can provide an alternative and non-invasive way for remote monitoring of rotating machinery [30].…”

Section: Introductionmentioning

confidence: 99%

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An Evaluation of Gearbox Condition Monitoring Using Infrared Thermal Images Applied with Convolutional Neural Networks

Zhen

et al. 2019

Sensors

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As an important machine component, the gearbox is widely used in industry for power transmission. Condition monitoring (CM) of a gearbox is critical to provide timely information for undertaking necessary maintenance actions. Massive research efforts have been made in the last two decades to develop vibration-based techniques. However, vibration-based methods usually include several inherent shortages including contact measurement, localized information, noise contamination, and high computation costs, making it difficult to be a cost-effective CM technique. In this paper, infrared thermal (IRT) images, which can contain information covering a large area and acquired remotely, are based on developing a cost-effective CM method. Moreover, a convolutional neural network (CNN) is employed to automatically process the raw IRT images for attaining more comprehensive feature parameters, which avoids the deficiency of incomplete information caused by various feature-extraction methods in vibration analysis. Thus, an IRT–CNN method is developed to achieve online remote monitoring of a gearbox. The performance evaluation based on a bevel gearbox shows that the proposed method can achieve nearly 100% correctness in identifying several common gear faults such as tooth pitting, cracks, and breakages and their compounds. It is also especially robust to ambient temperature changes. In addition, IRT also significantly outperforms its vibration-based counterparts.

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Section: Experimental Irt Images Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Evaluation of Gearbox Condition Monitoring Using Infrared Thermal Images Applied with Convolutional Neural Networks

Zhen

et al. 2019

Sensors

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“…In recent years, the Gini index as a measure of sparsity has been widely demonstrated in other areas, such as the speech signal process [27] and EEG signal analysis [28,29], etc. In the field of machinery fault diagnosis, the Gini index has been applied in feature extraction and proved useful [30][31][32]. The Gini index of signal x is defined as…”

Section: Gini Indexmentioning

confidence: 99%

Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification

Miao

Zhao

Lin

2017

Meas. Sci. Technol.

188

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A group of kurtosis-guided-grams, such as Kurtogram, Protrugram and SKRgram, is designed to detect the resonance band excited by faults based on the sparsity index. However, a common issue associated with these methods is that they tend to choose the frequency band with individual impulses rather than the desired fault impulses. This may be attributed to the selection of the sparsity index, kurtosis, which is vulnerable to impulsive noise. In this paper, to solve the problem, a sparsity index, called the Gini index, is introduced as an alternative estimator for the selection of the resonance band. It has been found that the sparsity index is still able to provide guidelines for the selection of the fault band without prior information of the fault period. More importantly, the Gini index has unique performance in random-impulse resistance, which renders the improved methods using the index free from the random impulse caused by external knocks on the bearing housing, or electromagnetic interference. By virtue of these advantages, the improved methods using the Gini index not only overcome the shortcomings but are more effective under harsh working conditions, even in the complex structure. Finally, the comparison between the kurtosis-guided-grams and the improved methods using the Gini index is made using the simulated and experimental data. The results verify the effectiveness of the improvement by both the fixed-axis bearing and planetary bearing fault signals.

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“…For the rotating machinery in operation, it is usually difficult to arrange corresponding sensors on its surface due to the rotating action of its components, and it is impossible to directly obtain the state information of the rotating machinery. When rotating machinery breaks down, conditions different from those of normal operating equipment usually occur, such as abnormal vibration and temperature change [ 21 ]. Therefore, infrared thermography (IRT) has gradually become a new non-destructive testing technology for measuring equipment temperature changes in recent years.…”

Section: Introductionmentioning

confidence: 99%

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

Wang¹,

Zhan²,

Bai³

et al. 2022

Micromachines

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At present, rotating machinery is widely used in all walks of life and has become the key equipment in many production processes. It is of great significance to strengthen the condition monitoring of rotating machinery, timely diagnose and eliminate faults to ensure the safe and efficient operation of rotating machinery and improve the economic benefits of enterprises. When the state of a rotating machine deteriorates, the thermal energy that is much more than its normal operation will be generated due to the increase in the friction between the components or other factors. Therefore, using the infrared thermal camera to collect the infrared thermal images of rotating machinery and judge the health status of rotating machinery by observing the temperature distribution in the thermal images is often more rapid and effective than other technologies. Nevertheless, after decades of development, the research achievements of infrared thermography (IRT) and its application in various industrial fields are numerous and complex, and there is a lack of systematic sorting and summary of the achievements in this field. Accordingly, this paper summarizes the development and application of IRT as a non-contact and non-invasive tool for equipment condition monitoring and fault diagnosis, and introduces the basic theory of IRT, image processing technology and fault diagnosis methods of rotating machinery in detail. Finally, the review is summarized and some future potential topics are proposed, which will make the subject easier for beginners and non-experts to understand.

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Data-driven imbalance and hard particle detection in rotating machinery using infrared thermal imaging

Cited by 12 publications

References 18 publications

An Evaluation of Gearbox Condition Monitoring Using Infrared Thermal Images Applied with Convolutional Neural Networks

An Evaluation of Gearbox Condition Monitoring Using Infrared Thermal Images Applied with Convolutional Neural Networks

Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

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