Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring

Abdeljaber, Osama; Sassi, Sadok; Avcı, Onur; Kıranyaz, Serkan; Ibrahim, Abdelrahman; Gabbouj, Moncef

doi:10.1109/tie.2018.2886789

Cited by 108 publications

(48 citation statements)

References 36 publications

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“…The effectiveness of a 2-D CNN was demonstrated using numerical [22] and experimental [23] models of a benchmark structure by joining the data of 14 accelerometers. As an alternative, a 1-D CNN has attracted attention in electrocardiogram (ECG) detection, engine detection [24], and voltage/current detection of electronic equipment [25]. These studies confirmed the excellent performance of a 1-D CNN in damage detection.…”

Section: Introductionmentioning

confidence: 71%

Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network

Chen

Liu

et al. 2021

Sensors

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This paper presents a novel approach to substantially improve the detection accuracy of structural damage via a one-dimensional convolutional neural network (1-D CNN) and a decision-level fusion strategy. As structural damage usually induces changes in the dynamic responses of a structure, a CNN can effectively extract structural damage information from the vibration signals and classify them into the corresponding damage categories. However, it is difficult to build a large-scale sensor system in practical engineering; the collected vibration signals are usually non-synchronous and contain incomplete structure information, resulting in some evident errors in the decision stage of the CNN. In this study, the acceleration signals of multiple acquisition points were obtained, and the signals of each acquisition point were used to train a 1-D CNN, and their performances were evaluated by using the corresponding testing samples. Subsequently, the prediction results of all CNNs were fused (decision-level fusion) to obtain the integrated detection results. This method was validated using both numerical and experimental models and compared with a control experiment (data-level fusion) in which all the acceleration signals were used to train a CNN. The results confirmed that: by fusing the prediction results of multiple CNN models, the detection accuracy was significantly improved; for the numerical and experimental models, the detection accuracy was 10% and 16–30%, respectively, higher than that of the control experiment. It was demonstrated that: training a CNN using the acceleration signals of each acquisition point and making its own decision (the CNN output) and then fusing these decisions could effectively improve the accuracy of damage detection of the CNN.

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Section: Introductionmentioning

confidence: 71%

Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network

Chen

Liu

et al. 2021

Sensors

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“…the GMM-HMMs). This improvement can be partially attributed to the unique capacity of the CNNs in characterizing complex functions 44 since the CNNs can automatically integrate different levels (from low to high) of features in an end-to-end classification architecture. Moreover, the combination of CNN and MFCC has demonstrated its superiority in speech recognition and other applications of pattern recognition.…”

Section: Mfcc Feature Classification Using Cnn-based Methodsmentioning

confidence: 99%

If structure can exclaim: a novel robotic-assisted percussion method for spatial bolt-ball joint looseness detection

Wang

Mobiny

Nguyen

et al. 2020

Structural Health Monitoring

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In proportion to the immense construction of spatial structures is the emergence of catastrophes related to structural damages (e.g. loose connections), thus rendering personal injury and property loss. It is therefore essential to detect spatial bolt looseness. Current methods for detecting spatial bolt looseness mostly focus on contact-type measurement, which may not be practical in some cases. Thus, inspired by the sound-based human diagnostic approach, we develop a novel percussion method using the Mel-frequency cepstral coefficient and the memory-augmented neural network in this article. In comparison with current investigations, the main contribution of this article is the detection of multi-bolt looseness for the first time with higher accuracy than prior methods. In particular, in terms of new data obtained via similar joints, the memory-augmented neural network can help avoid inefficient relearn and assimilate new data to provide accurate prediction with only a few data samples, which effectively improves the robustness of detection. Furthermore, percussion was implemented with a robotic arm instead of manual operation, which preliminarily explores the potential of implementing automation applications in real industries. Finally, experimental results demonstrate the effectiveness of the proposed method, which can guide future development of cyber-physics systems for structural health detection.

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“…In a relatively short time, 1D CNNs have become popular with a stateof-the-art performance in various signal processing applications such as early arrhythmia detection in electrocardiogram (ECG) beats [47][48][49], structural health monitoring and structural damage detection [50][51][52][53][54], high power engine fault monitoring [55] and real-time monitoring of high-power circuitry [56]. Furthermore, two recent studies have utilized 1D CNNs for damage detection in bearings [57][58][59][60]. However, in the latter study conducted by Zhang et al [60], both single and ensemble of deep 1D CNN(s) were created to detect, localize, and quantify bearing faults.…”

Section: Introductionmentioning

confidence: 99%

1-D Convolutional Neural Networks for Signal Processing Applications

Kıranyaz

İnce

Abdeljaber

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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272

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During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application-specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and motor-fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publically shared in a dedicated website.

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Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring

Cited by 108 publications

References 36 publications

Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network

Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network

If structure can exclaim: a novel robotic-assisted percussion method for spatial bolt-ball joint looseness detection

1-D Convolutional Neural Networks for Signal Processing Applications

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