Bearing fault analysis utilizing fuzzy logic methodology for enhanced diagnostic accuracy

• Autorzy: Santoso Santoso , Hartayu Ratna , Ridho'i Ahmad , Andriawan Aris Heri , Pambudi Wahyu Setyo , Anwar Ahmad Nuril , Muharom Syahri

Identyfikatory

DOI

10.15199/48.2024.10.13

Warianty tytułu

PL

Analiza uszkodzeń łożysk wykorzystująca metodologię logiki rozmytej w celu zwiększenia dokładności diagnostyki

• Języki publikacji: EN

Abstrakty

EN

This research aims to design a tool that can be used to detect damage or malfunctions in induction motors, especially in the bearing part which is the main driving component. Using the MPU 6050 accelerometer sensor and sound sensor module with the Arduino Nano microcontroller and the HC 05 Bluetooth module as a medium for sending and acquiring signal data. The signal data obtained in the form of a datalog with the extension .txt is then processed further with Matlab software to find out information on the characteristics of sound signals and vibration signals generated by induction motor bearings. Using a cut-off signal filter low pass filter for voice signal filter processing and fast Fourier transform (FFT) to convert the time domain signal into a signal frequency domain to determine the frequency characteristics arising from the signal. From the sound and vibration signal input data obtained, the Fuzzy logic method is used to determine the bearing condition output. The developed system is capable of detecting bearings in three conditions, namely good, damaged, and alert.

PL

Celem badań jest zaprojektowanie narzędzia, które będzie można wykorzystać do wykrywania uszkodzeń lub usterek w silnikach indukcyjnych, zwłaszcza w części łożyskowej będącej głównym elementem napędowym. Wykorzystanie czujnika akcelerometru i modułu czujnika dźwięku MPU 6050 z mikrokontrolerem Arduino Nano i modułem Bluetooth HC 05 jako medium do przesyłania i pozyskiwania danych sygnałowych. Uzyskane dane sygnałowe w postaci datalogu z rozszerzeniem .txt są następnie przetwarzane w programie Matlab w celu uzyskania informacji o charakterystyce sygnałów dźwiękowych i sygnałów wibracyjnych generowanych przez łożyska silników indukcyjnych. Zastosowanie filtra dolnoprzepustowego filtra sygnału odcinającego do przetwarzania filtra sygnału głosowego i szybkiej transformaty Fouriera (FFT) w celu konwersji sygnału w dziedzinie czasu na dziedzinę częstotliwości sygnału w celu określenia charakterystyk częstotliwości wynikających z sygnału. Na podstawie uzyskanych danych wejściowych sygnałów dźwiękowych i wibracyjnych metoda Fuzzy logic służy do określenia wyjściowego stanu łożyska. Opracowany system jest w stanie wykryć łożyska w trzech stanach: dobre, uszkodzone i czujne.

Słowa kluczowe

EN

sensor; Sound Sensor; bearing; fault; fuzzy logic

PL

czujnik; czujnik dźwięku; łożysko; usterka; logika rozmyta

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 10
• Strony: 73--78
• Opis fizyczny: Bibliogr. 62 poz., rys., tab.

Twórcy

autor

Santoso Santoso

• Department of Electrical Engineering, Engineering Faculty, Universitas 17 Augustus 1945 Surabaya, Indonesi

• https://orcid.org/0009-0001-8656-9665

autor

Hartayu Ratna

• Department of Electrical Engineering, Engineering Faculty, Universitas 17 Augustus 1945 Surabaya, Indonesi

• https://orcid.org/0009-0001-9938-7182

autor

Ridho'i Ahmad

• Department of Electrical Engineering, Engineering Faculty, Universitas 17 Augustus 1945 Surabaya, Indonesi

autor

Andriawan Aris Heri

• Department of Electrical Engineering, Engineering Faculty, Universitas 17 Augustus 1945 Surabaya, Indonesi

• https://orcid.org/0000-0002-1879-8431

autor

Pambudi Wahyu Setyo

• Department of Electrical Engineering, Institut Teknologi Adhi tama Surabaya, Indonesia

• https://orcid.org/0000-0001-7180-1102

autor

Anwar Ahmad Nuril

• Department of Electrical Engineering, Engineering Faculty, Universitas 17 Augustus 1945 Surabaya, Indonesi

autor

Muharom Syahri

• Department of Electrical Engineering, Institut Teknologi Adhi tama Surabaya, Indonesia

• https://orcid.org/0000-0002-8030-632X

Bibliografia

• [1] L. Zhan, F. Ma, Z. Li, H. Li, and C. Li, ‘Development of a Novel Detection Method to Measure the Cage Slip of Rolling Bearing’, IEEE Access, vol. 8, pp. 41929–41935, 2020, doi: 10.1109/ACCESS.2020.2976504.
• [2] S. Nath, J. Wu, Y. Zhao, and W. Qiao, ‘Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current’, IEEE Access, vol. 8, pp. 44163–44174, 2020, doi: 10.1109/ACCESS.2020.2977632.
• [3] W. Mao, H. Shi, G. Wang, and X. Liang, ‘Unsupervised Deep Multitask Anomaly Detection With Robust Alarm Strategy for Online Evaluation of Bearing Early Fault Occurrence’, IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–13, 2022, doi: 10.1109/TIM.2022.3200092.
• [4] V. Aviña-Corral, J. de Jesus Rangel-Magdaleno, H. Peregrina Barreto, and J. M. Ramirez-Cortes, ‘Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection’, IEEE Access, vol. 10, pp. 24181–24193, 2022, doi: 10.1109/ACCESS.2022.3154410.
• [5] T. Wang, Z. Liu, G. Lu, and J. Liu, ‘Temporal-Spatio Graph Based Spectrum Analysis for Bearing Fault Detection and Diagnosis’, IEEE Transactions on Industrial Electronics, vol. 68, no. 3, pp. 2598–2607, Mar. 2021, doi: 10.1109/TIE.2020.2975499.
• [6] J. Ma, S. Zhuo, C. Li, L. Zhan, and G. Zhang, ‘Study on Noncontact Aviation Bearing Faults and Speed Monitoring’, IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–21, 2021, doi: 10.1109/TIM.2021.3122913.
• [7] X. Huang et al., ‘Memory Residual Regression Autoencoder for Bearing Fault Detection’, IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–12, 2021, doi: 10.1109/TIM.2021.3072131.
• [8] D. Neupane and J. Seok, ‘Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review’, IEEE Access, vol. 8, pp. 93155–93178, 2020, doi: 10.1109/ACCESS.2020.2990528.
• [9] P. Xia, H. Xu, M. Lei, and S. Zhang, ‘An Improved Underdamped Asymmetric Bistable Stochastic Resonance Method and its Application for Spindle Bearing Fault Diagnosis’, IEEE Access, vol. 8, pp. 46824–46836, 2020, doi: 10.1109/ACCESS.2020.2976151.
• [10] P. Zhou, L. He, C. Yi, and Q. Zhou, ‘Impulses Recovery Technique Based on High Oscillation Region Detection and Shifted Rank-1 Reconstruction—Its Application to Bearing Fault Detection’, IEEE Sensors Journal, vol. 22, no. 8, pp. 8084 8093, Apr. 2022, doi: 10.1109/JSEN.2022.3159116.
• [11] Z. Chen, Y. Xia, H. Sheng, and J. He, ‘Analysis and Calibration of Blade Tip-Timing Vibration Measurement Under Variable Rotating Speeds’, IEEE Access, vol. 9, pp. 141467–141478, 2021, doi: 10.1109/ACCESS.2021.3120366.
• [12] M. Z. Ali, M. N. S. K. Shabbir, X. Liang, Y. Zhang, and T. Hu, ‘Machine Learning-Based Fault Diagnosis for Single- and Multi Faults in Induction Motors Using Measured Stator Currents and Vibration Signals’, IEEE Transactions on Industry Applications, vol. 55, no. 3, pp. 2378–2391, May 2019, doi: 10.1109/TIA.2019.2895797.
• [13] Y. Feng and Z. Liu, ‘Adaptive Vibration Iterative Learning Control of an Euler–Bernoulli Beam System With Input Saturation’, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2469–2477, Apr. 2023, doi: 10.1109/TSMC.2022.3214571.
• [14] M. Ojaghi, M. Sabouri, and J. Faiz, ‘Analytic Model for Induction Motors Under Localized Bearing Faults’, IEEE Transactions on Energy Conversion, vol. 33, no. 2, pp. 617 626, Jun. 2018, doi: 10.1109/TEC.2017.2758382.
• [15] Q. Jiang, F. Chang, and B. Sheng, ‘Bearing Fault Classification Based on Convolutional Neural Network in Noise Environment’, IEEE Access, vol. 7, pp. 69795–69807, 2019, doi: 10.1109/ACCESS.2019.2919126.
• [16] D. Liu, W. Cheng, and W. Wen, ‘Rolling Bearing Fault Diagnosis via ConceFT-Based Time-Frequency Reconfiguration Order Spectrum Analysis’, IEEE Access, vol. 6, pp. 67131–67143, 2018, doi: 10.1109/ACCESS.2018.2877711.
• [17] N. Nicusor, L. Baicu, and B. Dumitrascu, ‘Vibration spectrum analysis using FFT in the microcontroller’, in 2022 IEEE 28th International Symposium for Design and Technology in Electronic Packaging (SIITME), Oct. 2022, pp. 176–180. doi: 10.1109/SIITME56728.2022.9987820.
• [18] Y. Hawwari, J. Antoni, H. André, Y. Marnissi, D. Abboud, and M. El-Badaoui, ‘Robust Spectral Peaks Detection in Vibration and Acoustic Signals’, IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–13, 2022, doi: 10.1109/TIM.2022.3187742.
• [19] X. Wang, P. Wang, Q. Li, H. Wang, M. Xia, and R. Wang, ‘Vibration Signal Processing Method of GIS Equipment Based on Wavelet Analysis and Spectrum Analysis’, in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Mar. 2019, pp. 1215 1219. doi: 10.1109/ITNEC.2019.8729207.
• [20] J. Yu, T. Hu, and H. Liu, ‘A New Morphological Filter for Fault Feature Extraction of Vibration Signals’, IEEE Access, vol. 7, pp. 53743–53753, 2019, doi: 10.1109/ACCESS.2019.2912898.
• [21] Y. Sun and J. Yu, ‘Fault Detection of Rolling Bearing Using Sparse Representation-Based Adjacent Signal Difference’, IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–16, 2021, doi: 10.1109/TIM.2020.3044336.
• [22] H. T. Shi, X. T. Bai, K. Zhang, Z. N. Wang, and Z. M. Liu, ‘Spalling Localization on the Outer Ring of Hybrid Ceramic Ball Bearings Based on the Sound Signals’, IEEE Access, vol. 7, pp. 134621–134634, 2019, 10.1109/ACCESS.2019.2941982. doi:
• [23] V. S. C. S. Vaddadi, S. R. Parne, V. V. Parambil, S. S. S. Panda, and S. Gandi, ‘Design of Fiber Bragg Grating Sensor for Eccentricity Measurements in Ball Bearings’, IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–9, 2023, doi: 10.1109/TIM.2022.3220298.
• [24] C. Li, J. Li, X. L. Fang, Y. Tang, T. Deng, and Y. Lu, ‘Application of a Method of Identifiying Instantaneous Shaft Speed from Spectrum in Aeroengine Vibration Analysis’, in 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai), Oct. 2022, pp. 1–7. doi: 10.1109/PHM-Yantai55411.2022.9942056.
• [25] K. Zhang and X. Gu, ‘Rolling Bearing Vibration Signal Feature Extraction Based on SVD and Mathematical Morphological Fractal Dimension Spectrum’, in 2022 5th International Conference on Information Communication and Signal Processing (ICICSP), Nov. 2022, pp. 75–79. doi: 10.1109/ICICSP55539.2022.10050578.
• [26] F. Lyu, Y. Li, Y. Zhang, X. Fang, F. Li, and C. Hu, ‘Downhole Vibration Acquisition Method based on Compressed Sensing Technology’, in 2022 4th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP), Jul. 2022, pp. 460–464. doi: 10.1109/ICMSP55950.2022.9859126.
• [27] Y. Ding, Y. Zhong, X. Wang, J. Hao, and X. Jiang, ‘Difference and Analysis of Mechanical Vibration Signal Characteristics between Mechanical Defects and Partial Discharge Defects of Gas Insulated Switchgear’, in 2021 IEEE 5th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), Dec. 2021, pp. 211–214. doi: 10.1109/CATCON52335.2021.9670536.
• [28] X. Liu, X. Lu, Y. Ma, L. He, H. Ma, and J. Wu, ‘Mechanical Fault Diagnosis of Transformer Based on Vibration Signal Spectrum Centroid’, in 2020 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Sep. 2020, pp. 1–5. doi: 10.1109/APPEEC48164.2020.9220365.
• [29] J. L. Contreras-Hernandez, D. L. Almanza-Ojeda, S. Ledesma Orozco, A. Garcia-Perez, R. J. Romero-Troncoso, and M. A. Ibarra-Manzano, ‘Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection’, IEEE Transactions on Industrial Electronics, vol. 66, no. 11, pp. 8843–8850, Nov. 2019, doi: 10.1109/TIE.2019.2891468.
• [30] S. Shao, R. Yan, Y. Lu, P. Wang, and R. X. Gao, ‘DCNN Based Multi-Signal Induction Motor Fault Diagnosis’, IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 6, pp. 2658–2669, Jun. 2020, doi: 10.1109/TIM.2019.2925247.
• [31] A. K. Samanta, A. Routray, S. R. Khare, and A. Naha, ‘Minimum Distance-Based Detection of Incipient Induction Motor Faults Using Rayleigh Quotient Spectrum of Conditioned Vibration Signal’, IEEE Transactions on Instrumentation and Measurement, vol. 70, 10.1109/TIM.2020.3047433. pp. 1–11, 2021, doi:
• [32] X. Liang, ‘Temperature Estimation and Vibration Monitoring for Induction Motors and the Potential Application in Electrical Submersible Motors’, Canadian Journal of Electrical and Computer Engineering, vol. 42, no. 3, pp. 148–162, 2019, doi: 10.1109/CJECE.2018.2875111.
• [33] M. Z. Ali and X. Liang, ‘Threshold-Based Induction Motors Single- and Multifaults Diagnosis Using Discrete Wavelet Transform and Measured Stator Current Signal’, Canadian Journal of Electrical and Computer Engineering, vol. 43, no. 3, pp. 136–145, 2020, doi: 10.1109/CJECE.2020.2966114.
• [34] T. Liu, H. Zhu, M. Wu, and W. Zhang, ‘Rotor Displacement Self-Sensing Method for Six-Pole Radial Hybrid Magnetic Bearing Using Mixed-Kernel Fuzzy Support Vector Machine’, IEEE Transactions on Applied Superconductivity, vol. 30, no. 4, pp. 1–4, Jun. 2020, doi: 10.1109/TASC.2020.2990415.
• [35] C. Li, J. V. de Oliveira, M. Cerrada, D. Cabrera, R. V. Sánchez, and G. Zurita, ‘A Systematic Review of Fuzzy Formalisms for Bearing Fault Diagnosis’, IEEE Transactions on Fuzzy Systems, vol. 27, no. 7, pp. 1362–1382, Jul. 2019, doi: 10.1109/TFUZZ.2018.2878200.
• [36] X. Sun and X. Jia, ‘A Fault Diagnosis Method of Industrial Robot Rolling Bearing Based on Data Driven and Random Intuitive Fuzzy Decision’, IEEE Access, vol. 7, pp. 148764 148770, 2019, doi: 10.1109/ACCESS.2019.2944974.
• [37] Y. Jiang, J. Weng, X. Zhang, Z. Yang, and W. Hu, ‘A CNN Based Born-Again TSK Fuzzy Classifier Integrating Soft Label Information and Knowledge Distillation’, IEEE Transactions on Fuzzy Systems, vol. 31, no. 6, pp. 1843–1854, Jun. 2023, doi: 10.1109/TFUZZ.2022.3215566.
• [38] Q. T. Tran, S. D. Nguyen, and T.-I. Seo, ‘Algorithm for Estimating Online Bearing Fault Upon the Ability to Extract Meaningful Information From Big Data of Intelligent Structures’, IEEE Transactions on Industrial Electronics, vol. 66, no. 5, pp. 3804–3813, May 2019, doi: 10.1109/TIE.2018.2847704.
• [39] A. Hassan et al., ‘Statistical scheme for fault detection using Arduino and MPU 6050’, in 2019 Prognostics and System Health Management Conference (PHM-Qingdao), Oct. 2019, pp. 1–7. doi: 10.1109/PHM-Qingdao46334.2019.8942922.
• [40] J. Li, B. Shen, D. Zhao, W. Zhang, and B. Sun, ‘A High sensitivity FBG Accelerometer Based on a Bearing’, Journal of Lightwave Technology, vol. 40, no. 1, pp. 228–236, Jan. 2022, doi: 10.1109/JLT.2021.3117867.
• [41] I. Koene, R. Viitala, and P. Kuosmanen, ‘Internet of Things Based Monitoring of Large Rotor Vibration With a Microelectromechanical Systems Accelerometer’, IEEE Access, vol. 7, pp. 92210–92219, 10.1109/ACCESS.2019.2927793. 2019, doi:
• [42] M. Y. E. Simik, F. Chi, and C. L. Wei, ‘Design and Implementation of a Bluetooth-Based MCU and GSM for Wetness Detection’, IEEE Access, vol. 7, pp. 21851–21856, 2019, doi: 10.1109/ACCESS.2019.2897324.
• [43] W. Albazrqaoe, J. Huang, and G. Xing, ‘A Practical Bluetooth Traffic Sniffing System: Design, Implementation, and Countermeasure’, IEEE/ACM Transactions on Networking, vol. 27, no. 1, pp. 71–84, Feb. 10.1109/TNET.2018.2880970. 2019, doi:
• [44] E. Song, S. Kim, J. Han, and K. Kwon, ‘A 2.4-GHz Quadrature Local Oscillator Buffer Insensitive to Frequency-Dependent Loads for Bluetooth Low Energy Applications’, IEEE Microwave and Wireless Components Letters, vol. 30, no. 10, pp. 961 964, Oct. 2020, doi: 10.1109/LMWC.2020.3016733.
• [45] A. Zarinbal Masouleh and B. R. Hellinga, ‘Simulation of the Bluetooth Inquiry Process for Application in Transportation Engineering’, IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 1020–1030, Mar. 2019, doi: 10.1109/TITS.2018.2841402.
• [46] Z. Wen, G. Yang, Q. Cai, and T. Chen, ‘A Novel Bluetooth Odometer-Aided Smartphone-Based Vehicular Navigation in Satellite-Denied Environments’, IEEE Transactions on Industrial Electronics, vol. 70, no. 3, pp. 3136–3146, Mar. 2023, doi: 10.1109/TIE.2022.3169714.
• [47] W.-L. Mao and C.-T. Chu, ‘Modeless Magnetic Bearing System Tracking Using an Adaptive Fuzzy Hermite Neural Network Method’, IEEE Sensors Journal, vol. 19, no. 14, pp. 5904 5915, Jul. 2019, doi: 10.1109/JSEN.2019.2906730.
• [48] G.-P. Ren et al., ‘Design of Interval Type-2 Fuzzy Controllers for Active Magnetic Bearing Systems’, IEEE/ASME Trans. Mechatron., vol. 25, no. 5, pp. 2449–2459, Oct. 2020, doi: 10.1109/TMECH.2020.2978018.
• [49] M. Rostaghi, M. M. Khatibi, M. R. Ashory, and H. Azami, ‘Fuzzy Dispersion Entropy: A Nonlinear Measure for Signal Analysis’, IEEE Transactions on Fuzzy Systems, vol. 30, no. 9, pp. 3785 3796, Sep. 2022, doi: 10.1109/TFUZZ.2021.3128957.
• [50] W. Du et al., ‘A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis’, Entropy, vol. 22, no. 1, Art. no. 1, Jan. 2020, doi: 10.3390/e22010027.
• [51] P. Chotikunnan, R. Chotikunnan, A. Nirapai, A. Wongkamhang, P. Imura, and M. Sangworasil, ‘Optimizing Membership Function Tuning for Fuzzy Control of Robotic Manipulators Using PID-Driven Data Techniques’, Journal of Robotics and Control (JRC), vol. 4, no. 2, Art. no. 2, Mar. 2023, doi: 10.18196/jrc.v4i2.18108.
• [52] H. Talhaoui, T. Ameid, O. Aissa, and A. Kessal, ‘Wavelet packet and fuzzy logic theory for automatic fault detection in induction motor’, Soft Comput, vol. 26, no. 21, pp. 11935 11949, Nov. 2022, doi: 10.1007/s00500-022-07028-5.
• [53] M. T. Pham, J.-M. Kim, and C. H. Kim, ‘Rolling Bearing Fault Diagnosis Based on Improved GAN and 2-D Representation of Acoustic Emission Signals’, IEEE Access, vol. 10, pp. 78056 78069, 2022, doi: 10.1109/ACCESS.2022.3193244.
• [54] Z. Zhao, S. Wang, D. Wong, W. Wang, R. Yan, and X. Chen, ‘Fast Sparsity-Assisted Signal Decomposition With Nonconvex Enhancement for Bearing Fault Diagnosis’, IEEE/ASME Transactions on Mechatronics, vol. 27, no. 4, pp. 2333–2344, Aug. 2022, doi: 10.1109/TMECH.2021.3103287.
• [55] H. Tang, S. Lu, G. Qian, J. Ding, Y. Liu, and Q. Wang, ‘IoT Based Signal Enhancement and Compression Method for Efficient Motor Bearing Fault Diagnosis’, IEEE Sensors Journal, vol. 21, no. 2, pp. 1820–1828, Jan. 2021, doi: 10.1109/JSEN.2020.3017768.
• [56] J. Wang, W. Qiao, and L. Qu, ‘Wind Turbine Bearing Fault Diagnosis Based on Sparse Representation of Condition Monitoring Signals’, IEEE Transactions on Industry Applications, vol. 55, no. 2, pp. 1844–1852, Mar. 2019, doi: 10.1109/TIA.2018.2873576.
• [57] R. Duan, Y. Liao, L. Yang, and E. Song, ‘Faulty Bearing Signal Analysis With Empirical Morphological Undecimated Wavelet’, IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–11, 2022, doi: 10.1109/TIM.2022.3160551.
• [58] M. Zhang, J. Yin, and W. Chen, ‘Rolling Bearing Fault Diagnosis Based on Time-Frequency Feature Extraction and IBA-SVM’, IEEE Access, vol. 10, pp. 85641–85654, 2022, doi: 10.1109/ACCESS.2022.3198701.
• [59] K. Doğançay, W. Y. Wang, and N. Hung Nguyen, ‘Bias Compensated Diffusion Pseudolinear Kalman Filter Algorithm for Censored Bearings-Only Target Tracking’, IEEE Signal Processing Letters, vol. 26, no. 11, pp. 1703–1707, Nov. 2019, doi: 10.1109/LSP.2019.2945677.
• [60] T. Zhang, Q. Wang, Y. Shu, W. Xiao, and W. Ma, ‘Remaining Useful Life Prediction for Rolling Bearings With a Novel Entropy-Based Health Indicator and Improved Particle Filter Algorithm’, IEEE Access, vol. 11, pp. 3062–3079, 2023, doi: 10.1109/ACCESS.2023.3234286.
• [61] D. Zhang, X. Ding, W. Huang, and Q. He, ‘Transient Signal Analysis Using Parallel Time-Frequency Manifold Filtering for Bearing Health Diagnosis’, IEEE Access, vol. 7, pp. 175277 175289, 2019, doi: 10.1109/ACCESS.2019.2956824.
• [62] T. Duan, Z. Liao, T. Li, H. Tang, and P. Chen, ‘Bearing Fault Diagnosis Based on State-Space Principal Component Tracking Filter Algorithm’, IEEE Access, vol. 9, pp. 158784 158795, 2021, doi: 10.1109/ACCESS.2021.3131494.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).