Abstract
Real-time health condition monitoring of bearings plays a significant role in the functionality of the rotary machinery. Multi-channel sensor fusion can be more robust for identifying diverse bearing fault diagnosis scenarios. However, the high-dimensional data and complex fault scenarios that can occur in the system pose significant challenges for effective fault diagnosis. State-of-the-art artificial intelligence-based bearing fault diagnosis system involves multi-channel sensor fusion, which usually leverages time–frequency analysis, feature extraction, and supervised learning. Nevertheless, those methods usually require a large training dataset for the machine learning model development. This paper proposes a new multi-channel sensor fusion methodology, named frequency-domain multilinear principal component analysis (FDMPCA), by integrating acoustics and vibration signals with different sampling rates and limited training data. Frequency analysis is firstly leveraged to transform the original signals from time to frequency domain, and the frequency responses of heterogeneous channels form a tensor structure named the frequency-domain (FD) tensor. Subsequently, the FD tensor is decomposed by multilinear principal component analysis (MPCA), resulting in low-dimensional process features for fault diagnosis. Finally, the extracted features can be used to train a Neural Network (NN) model for fault diagnosis. To validate the effectiveness of the proposed method, the bearing fault experiments were conducted on a machinery fault simulator while multiple vibration and acoustic signals were collected. Experimental results demonstrated that the proposed approach can effectively identify the machine fault conditions and outperform the benchmark methods given the limited training data.
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AlShorman O, Irfan M, Saad N, Zhen D, Haider N, Glowacz A, AlShorman A (2020) A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor. Shock Vib 2020(8843759):1–20. https://doi.org/10.1155/2020/8843759
Tiboni M, Remino C, Bussola R, Amici C (2022) A review on vibration-based condition monitoring of rotating machinery. Appl Sci 12(3):972. https://doi.org/10.3390/app12030972
Wang Z, Lu C, Zhou B (2018) Fault diagnosis for rotary machinery with selective ensemble neural networks. Mech Syst Signal Process 113:112–130. https://doi.org/10.1016/j.ymssp.2017.03.051
Lu C, Wang Y, Ragulskis M, Cheng Y (2016) Fault diagnosis for rotating machinery: a method based on image processing. PLoS ONE 11(10):1–22. https://doi.org/10.1371/journal.pone.0164111
Maleki E, Belkadi F, Boli N, van der Zwaag BJ, Alexopoulos K, Koukas S, Marin-Perianu M, Bernard A, Mourtzis D (2018) Ontology-based framework enabling smart product-service systems: application of sensing systems for machine health monitoring. IEEE Internet Things J 5(6):4496–4505. https://doi.org/10.1109/JIOT.2018.2831279
Yan X, Liu Y, Jia M (2020) Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions. Knowl-Based Syst 193:105484. https://doi.org/10.1016/j.knosys.2020.105484
Luo RC, Chang CC, Lai CC (2011) Multisensor fusion and integration: theories, applications, and its perspectives. IEEE Sens J 11(12):3122–3138. https://doi.org/10.1109/JSEN.2011.2166383
Baydar N, Ball A (2003) Detection of gear failures via vibration and acoustic signals using wavelet transform. Mech Syst Signal Process 17(4):787–804. https://doi.org/10.1006/mssp.2001.1435
Henriquez P, Alonso JB, Ferrer MA, Travieso CM (2014) Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Trans Syst Man Cybernetics: Syst 44(5):642–652. https://doi.org/10.1109/TSMCC.2013.2257752
Tandon N, Nakra BC (1992) Comparison of vibration and acoustic measurement techniques for the condition monitoring of rolling element bearings. Tribol Int 25(3):205–212. https://doi.org/10.1016/0301-679X(92)90050-W
Banerjee TP, Das S (2012) Multi-sensor data fusion using support vector machine for motor fault detection. Inf Sci 217:96–107. https://doi.org/10.1016/j.ins.2012.06.016
Kateris D, Moshou D, Pantazi X-E, Gravalos I, Sawalhi N, Loutridis S (2014) A machine learning approach for the condition monitoring of rotating machinery. J Mech Sci Technol 28(1):61–71. https://doi.org/10.1007/s12206-013-1102-y
Sikder N, Bhakta K, Al Nahid A, Islam MMM (2019)\ Fault diagnosis of motor bearing using ensemble learning algorithm with FFT-based preprocessing. 1st International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2019:564–569. https://doi.org/10.1109/ICREST.2019.8644089
Vakharia V, Gupta VK, Kankar PK (2015) Ball bearing fault diagnosis using supervised and unsupervised machine learning methods. Int J Acoust Vib 20(4):244–250. https://doi.org/10.20855/ijav.2015.20.4387
Gunerkar RS, Jalan AK (2019) Classification of ball bearing faults using vibro-acoustic sensor data fusion. Exp Tech 43(5):635–643. https://doi.org/10.1007/s40799-019-00324-0
Wang X, Mao D, Li X (2021) Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Meas: J Int Meas Confederation 173(June 2020):108518. https://doi.org/10.1016/j.measurement.2020.108518
Hang Q, Yang J, Xing L (2019) Diagnosis of rolling bearing based on classification for high dimensional unbalanced data. IEEE Access 7:79159–79172. https://doi.org/10.1109/ACCESS.2019.2919406
Kolar D, Lisjak D, Pajak M, Gudlin M (2021) Intelligent fault diagnosis of rotary machinery by convolutional neural network with automatic hyper-parameters tuning using bayesian optimization. Sensors 21(7). https://doi.org/10.3390/s21072411
Mian T, Choudhary A, Fatima S (2021) A sensor fusion based approach for bearing fault diagnosis of rotating machine. Proc Inst Mech Eng Part O: J Risk Reliab 1–15.https://doi.org/10.1177/1748006X211044843
Wang X, Mao D, Li X (2021) Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Measurement 173(June 2020):108518. https://doi.org/10.1016/j.measurement.2020.108518
Yang BS, Kim KJ (2006) Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mech Syst Signal Process 20(2):403–420. https://doi.org/10.1016/j.ymssp.2004.10.010
Duan Z, Wu T, Guo S, Shao T, Malekian R, Li Z (2018) Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review. Int J Adv Manuf Technol 96(1–4):803–819. https://doi.org/10.1007/s00170-017-1474-8
Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510. https://doi.org/10.1016/j.ymssp.2005.09.012
Li X, Zhang W, Ding Q, Sun J-Q (2020) Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation. J Intell Manuf 31(2):433–452. https://doi.org/10.1007/s10845-018-1456-1
Li Y, Du X, Wan F, Wang X, Yu H (2020) Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging. Chin J Aeronaut 33(2):427–438. https://doi.org/10.1016/j.cja.2019.08.014
Currano LJ, Bauman S, Churaman W, Peckerar M, Wienke J, Kim S, Yu M, Balachandran B (2008) Latching ultra-low power MEMS shock sensors for acceleration monitoring. Sens Actuators, A 147(2):490–497. https://doi.org/10.1016/j.sna.2008.06.009
Van Tran H, Ngo TH, Tran NDK, Dang TN, Dao TP, Wang DA (2018) A threshold accelerometer based on a tristable mechanism. Mechatronics 53(May):39–55. https://doi.org/10.1016/j.mechatronics.2018.05.013
Wang T, Lu G, Yan P (2020) A novel statistical time-frequency analysis for rotating machine condition monitoring. IEEE Trans Industr Electron 67(1):531–541. https://doi.org/10.1109/TIE.2019.2896109
Chen S, Meng Y, Tang H, Tian Y, He N, Shao C (2020) Robust deep learning-based diagnosis of mixed faults in rotating machinery. IEEE/ASME Trans Mechatron 25(5):2167–2176. https://doi.org/10.1109/TMECH.2020.3007441
Hao S, Ge F, Li Y, Jiang J (2020) Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks. Measurement 159:107802. https://doi.org/10.1016/j.measurement.2020.107802
Junior RFR, Areias dos Santos IA, Campos MM, Teixeira CE, da Silva LEB, Gomes GF (2022) Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals. Meas: J Int Meas Conf 190(January):12–14. https://doi.org/10.1016/j.measurement.2022.110759
Stavropoulos P, Papacharalampopoulos A, Souflas T (2020) Indirect online tool wear monitoring and model-based identification of process-related signal. Adv Mech Eng 12(5):1–12. https://doi.org/10.1177/1687814020919209
Duro JA, Padget JA, Bowen CR, Kim HA, Nassehi A (2016) Multi-sensor data fusion framework for CNC machining monitoring. Mech Syst Signal Process 66–67:505–520. https://doi.org/10.1016/j.ymssp.2015.04.019
Yao Y, Wang H, Li S, Liu Z, Gui G, Dan Y, Hu J (2018) End-to-end convolutional neural network model for gear fault diagnosis based on sound signals. Appl Sci 8(9):1584. https://doi.org/10.3390/app8091584
Ciampa F, Mahmoodi P, Pinto F, Meo M (2018) Recent advances in active infrared thermography for non-destructive testing of aerospace components. Sensors 18(2):609. https://doi.org/10.3390/s18020609
Grammatikos SA, Kordatos EZ, Matikas TE, Paipetis AS (2018) On the fatigue response of a bonded repaired aerospace composite using thermography. Compos Struct 188(November 2017):461–469. https://doi.org/10.1016/j.compstruct.2018.01.035
Laborda A, Robinson A, Wang S, Zhang Y, Reed P (2018) Fatigue assessment of multilayer coatings using lock-in thermography. Mater Des 141:361–373. https://doi.org/10.1016/j.matdes.2018.01.004
Janssens O, Schulz R, Slavkovikj V, Stockman K, Loccufier M, Van De Walle R, Van Hoecke S (2015) Thermal image based fault diagnosis for rotating machinery. Infrared Phys Technol 73:78–87. https://doi.org/10.1016/j.infrared.2015.09.004
Shao H, Li W, Xia M, Zhang Y, Shen C, Williams D, Kennedy A, De Silva CW (2021) Fault diagnosis of a rotor-bearing system under variable rotating speeds using two-stage parameter transfer and infrared thermal images. IEEE Trans Instrum Meas 70.https://doi.org/10.1109/TIM.2021.3111977
Gravina R, Alinia P, Ghasemzadeh H, Fortino G (2017) Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Information Fusion 35:1339–1351. https://doi.org/10.1016/j.inffus.2016.09.005
Miao Q, Makis V (2007) Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models. Mech Syst Signal Process 21(2):840–855. https://doi.org/10.1016/j.ymssp.2006.01.009
Prasad T, Das S (2012) Multi-sensor data fusion using support vector machine for motor fault detection 217:96–107.https://doi.org/10.1016/j.ins.2012.06.016
Liu Q, Wang HP (2001) A case study on multisensor data fusion for imbalance diagnosis of rotating machinery. Artif Intell Eng Des Anal Manuf: AIEDAM 15(3):203–210. https://doi.org/10.1017/S0890060401153011
Stavropoulos P, Papacharalampopoulos A, Sabatakakis K, Mourtzis D (2021) Quality monitoring of manufacturing processes based on full data utilization. Procedia CIRP 104:1656–1661. https://doi.org/10.1016/j.procir.2021.11.279
Zebari R, Abdulazeez A, Zeebaree D, Zebari D, Saeed J (2020) A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. J Appl Sci Technol Trends 1(2):56–70. https://doi.org/10.38094/jastt1224
Li W, Gu F, Ball AD, Leung AYT, Phipps CE (2001) A study of the noise from diesel engines using the independent component analysis. Mech Syst Signal Process 15(6):1165–1184. https://doi.org/10.1006/mssp.2000.1366
Wang F, Sun J, Yan D, Zhang S, Cui L, Xu Y (2015) A feature extraction method for fault classification of rolling bearing based on PCA. J Phys: Conf Ser 628(1). https://doi.org/10.1088/1742-6596/628/1/012079
Yuan Y, Chen C (2020) Fault detection of rolling bearing based on principal component analysis and empirical mode decomposition. AIMS Mathematics 5(6):5916–5938. https://doi.org/10.3934/math.2020379
Zhang K, Li Y, Scarf P, Ball A (2011) Feature selection for high-dimensional machinery fault diagnosis data using multiple models and radial basis function networks. Neurocomputing 74(17):2941–2952. https://doi.org/10.1016/j.neucom.2011.03.043
Hu C, He S, Wang Y (2021) A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis. Appl Intell 51(4):2609–2621. https://doi.org/10.1007/s10489-020-02011-9
Wu Y, Yang F, Liu Y, Zha X, Yuan S (2018) A comparison of 1-D and 2-D deep convolutional neural networks in ECG classification. Computer Vision and Pattern Recognition, 48–51. http://arxiv.org/abs/1810.07088
Hoang DT, Kang HJ (2019) Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cogn Syst Res 53:42–50. https://doi.org/10.1016/j.cogsys.2018.03.002
Shao H, Jiang H, Zhao H, Wang F (2017) A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech Syst Signal Process 95:187–204. https://doi.org/10.1016/j.ymssp.2017.03.034
Dasarathy BV (1997) Sensor fusion potential exploitation-innovative architectures and illustrative applications. Proc IEEE 85(1):24–38. https://doi.org/10.1109/5.554206
Wang T, Lu G, Yan P (2019) Multi-sensors based condition monitoring of rotary machines: an approach of multidimensional time-series analysis. Meas: J Int Meas Confederation 134:326–335. https://doi.org/10.1016/j.measurement.2018.10.089
Wang HF, Wang JP (2000) Fault diagnosis theory: method and application based on multisensor data fusion. J Test Eval 28(6):513–518. https://doi.org/10.1520/jte12143j
Planet S, Iriondo I (2012) Comparison between decision-level and feature-level fusion of acoustic and linguistic features for spontaneous emotion recognition. 7th Iberian Conference on Information Systems and Technologies (CISTI 2012), June 2166–0735
Huang M, Liu Z, Tao Y (2020) Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion. Simulation Model Pract Theory 102(July2 019):101981. https://doi.org/10.1016/j.simpat.2019.101981
Guo H, Zhang Q, Nandi AK (2008) Feature extraction and dimensionality reduction by genetic programming based on the Fisher criterion. Expert Syst 25(5):444–459. https://doi.org/10.1111/j.1468-0394.2008.00451.x
Gunerkar RS, Jalan AK, Belgamwar SU (2019) Fault diagnosis of rolling element bearing based on artificial neural network. J Mech Sci Technol 33(2):505–511. https://doi.org/10.1007/s12206-019-0103-x
Wu J, Hao XC, Xiong ZL, Lei H (2019) Hyperparameter optimization for machine learning models based on Bayesian optimization. J Electr Sci Technol 17(1):26–40. https://doi.org/10.11989/JEST.1674-862X.80904120
Lu H, Plataniotis KN, Venetsanopoulos AN (2008) MPCA: Multilinear principal component analysis of tensor objects. IEEE Trans Neural Networks. https://doi.org/10.1109/TNN.2007.901277
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All authors contributed to the study conception and design. Experimental setup and data collection were performed jointly by Abdullah Al Manum, Ayantha Senanayaka Mudiyanselage, Jiali Li, and Zhipeng Jiang, and the methodology was developed jointly by Abdullah Al Manum, Mahathir Mohammad Bappy, and Wenmeng Tian. The first draft of the manuscript was written by Abdullah Al Manum, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Al Mamun, A., Bappy, M.M., Mudiyanselage, A.S. et al. Multi-channel sensor fusion for real-time bearing fault diagnosis by frequency-domain multilinear principal component analysis. Int J Adv Manuf Technol 124, 1321–1334 (2023). https://doi.org/10.1007/s00170-022-10525-4
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DOI: https://doi.org/10.1007/s00170-022-10525-4