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A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning. (2021)
Journal Article
HASAN, M.J., RAI, A., AHMAD, Z. and KIM, J.-Y. 2021. A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning. IEEE access [online], 9, pages 58052-58066. Available from: https://doi.org/10.1109/ACCESS.2021.3072854

Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal pump can affect imperative industrial processes. To ensure reliable operation of the centrifugal pump, this paper proposes a novel automated health state diagno... Read More about A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning..

Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning. (2021)
Journal Article
HASAN, M.J., ISLAM, M.M.M. and KIM, J.-M. 2022. Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning. Sensors [online], 22(1): sensing technologies for fault diagnostics and prognosis, article 56. Available from: https://doi.org/10.3390/s22010056

Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-do... Read More about Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning..

A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test. (2021)
Journal Article
RAI, A., AHMAD, Z., HASAN, M.J. and KIM, J.-M. 2021. A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test. Sensors [online], 21(24): intelligent systems for fault diagnosis and prognosis, article 8247. Available from: https://doi.org/10.3390/s21248247

Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and ent... Read More about A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test..

A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of Walsh transform and cosine linear discriminant analysis. (2021)
Journal Article
AHMAD, Z., RAI, A., HASAN, M.J., KIM, C.H. and KIM, J.-M. 2021. A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of walsh transform and cosine linear discriminant analysis. IEEE access [online], 9, pages 150128-150141. Available from: https://doi.org/10.1109/ACCESS.2021.3124903

In this paper, we propose a three-stage lightweight framework for centrifugal pump fault diagnosis. First, the centrifugal pump vibration signatures are fast transformed using a Walsh transform, and Walsh spectra are obtained. To overcome the hefty n... Read More about A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of Walsh transform and cosine linear discriminant analysis..

A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals. (2021)
Journal Article
HABIB, M.A., HASAN, M.J. and KIM, J.-M. 2021. A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals. IEEE access [online], 9, pages 104029-104050. Available from: https://doi.org/10.1109/ACCESS.2021.3099124

This paper proposes an acoustic emission (AE) based automated crack characterization method for reinforced concrete (RC) beams using a memory efficient lightweight convolutional neural network named SqueezeNet. The proposed method also includes a sig... Read More about A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals..

An explainable AI-based fault diagnosis model for bearings. (2021)
Journal Article
HASAN, M.J., SOHAIB, M. and KIM, J.-M. 2021. An explainable AI-based fault diagnosis model for bearings. Sensors [online], 21(12): sensing technologies for fault diagnostics and prognosis, article 4070. Available from: https://doi.org/10.3390/s21124070

In this paper, an explainable AI-based fault diagnosis model for bearings is proposed with five stages, i.e., (1) a data preprocessing method based on the Stockwell Transformation Coefficient (STC) is proposed to analyze the vibration signals for var... Read More about An explainable AI-based fault diagnosis model for bearings..