Abstract
Previous research investigators have exploited machine-learning algorithms to diagnose the defects in rotating machinery. However, with increasing complexity in the design of rotating machinery, it is quite challenging to quantify the faults precisely. In this present study, an attempt has been made to predict the defect severity of the rotating machinery using Adaptive Neuro-Fuzzy Inference System (ANFIS). This ANFIS algorithm employs artificial neural networks to define the membership functions, rules and weights to construct the fuzzy inference system. Experiments are performed on a multi-stage spur gearbox model while it is subjected to fluctuating operating speeds. Two local defects on bearing race as well as on gear tooth with four different severity levels are seeded intentionally. Three condition monitoring (CM) strategies, namely, vibration, lubrication oil and acoustic signal analyses are executed, and the raw data is recorded synchronously. The raw vibration and acoustic waveforms are decomposed through discrete wavelet transform to extract the descriptive statistics from the wavelet coefficients. Among them, most discriminating features are selected and given as input to ANFIS classification tool to train the network for obtaining the Sugeno-type FIS, which in turn estimates the severity of the component. Later, the features from the individual CM strategies are combined to devise an integrated feature dataset which is further channelled as input to the ANFIS for predicting the defect severity levels. The investigation reveals that, the proposed integrated feature set in conjunction with ANFIS can discriminate between the defect severity conditions of the gears as well as bearings under fluctuating speeds.
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Data Availability
The datasets acquired and analysed during the current study are available from the corresponding author on reasonable request.
References
Huitao C, Shuangxi J, Xianhui W, Zhiyang W (2018) Fault diagnosis of wind turbine gearbox based on wavelet neural network. J Low Freq Noise Vib Active Control 37(4):977–986
Zhu K, Chen L, Hu X (2019) A multi-scale fuzzy measure entropy and infinite feature selection based approach for rolling bearing fault diagnosis. J Nondestr Eval 38(4):90
Amarnath M, Sugumaran V, Kumar H (2013) Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement 46(3):1250–1256
Kumar A, Gandhi CP, Zhou Y, Kumar R, Xiang J (2020) Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images. Appl Acoust 167:107399
Kattelus J, Miettinen J, Lehtovaara A (2018) Detection of gear pitting failure progression with on-line particle monitoring. Tribol Int 118:458–464
Kumar A, Gandhi CP, Zhou Y, Kumar R, Xiang J (2020) Variational mode decomposition based symmetric single valued neutrosophic cross entropy measure for the identification of bearing defects in a centrifugal pump. Appl Acoust 165:107294
Amarnath M, Krishna IP (2014) Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis. Measurement 58:154–164
Mohanty S, Gupta KK, Raju KS (2018) Hurst based vibro-acoustic feature extraction of bearing using EMD and VMD. Measurement 117:200–220
Vamsi I, Sabareesh GR, Penumakala PK (2019) Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading. Mech Syst Signal Process 124:1–20
Loutas TH, Roulias D, Pauly E, Kostopoulos V (2011) The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mech Syst Signal Process 25(4):1339–1352
Nembhard AD, Sinha JK, Pinkerton AJ, Elbhbah K (2014) Combined vibration and thermal analysis for the condition monitoring of rotating machinery. Struct Health Monit 13(3):281–295
Yan R, Gao RX, Chen X (2014) Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process 96:1–15
Praveen G, Vamsi I, Suresh K, Radhika S (2020) Evaluation of surface roughness in incremental forming using image processing based methods. Measurement 108055
Inturi V, Sabareesh GR, Supradeepan K, Penumakala PK (2019) Integrated condition monitoring scheme for bearing fault diagnosis of a wind turbine gearbox. J Vib Control 25(12):1852–1865
Sugumaran V, Sabareesh GR, Ramachandran KI (2008) Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine. Expert Syst Appl 34(4):3090–3098
Inturi V, Sachin PR, Sabareesh GR (2020) Supervised feature selection methods for fault diagnostics at different speed stages of a wind turbine gearbox. In: International conference on modelling, simulation and intelligent computing, Springer, Singapore, pp 478–486
Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using machine learning methods. Expert Syst Appl 38(3):1876–1886
Ali JB, Fnaiech N, Saidi L, Chebel-Morello B, Fnaiech F (2015) Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust 89:16–27
Wang YS, Ma QH, Zhu Q, Liu XT, Zhao LH (2014) An intelligent approach for engine fault diagnosis based on Hilbert-Huang transform and support vector machine. Appl Acoust 75:1–9
Balavignesh VN, Gundepudi B, Sabareesh GR, Vamsi I (2018) Comparison of conventional method of fault determination with data-driven approach for ball bearings in a wind turbine gearbox. Int J Perform Eng 14(3):397
Liu R, Yang B, Zio E, Chen X (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Signal Process 108:33–47
Wu JD, Hsu CC (2009) Fault gear identification using vibration signal with discrete wavelet transform technique and fuzzy–logic inference. Expert Syst Appl 36(2):3785–3794
Abdulshahed AM, Longstaff AP, Fletcher S (2015) The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Appl Soft Comput 27:158–168
Lou X, Loparo KA (2004) Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech Syst Signal Process 18(5):1077–1095
Henriquez P, Alonso JB, Ferrer MA, Travieso CM (2014) Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Trans Syst Man Cybern Syst 44(5):642–652
Inturi V, Sabareesh GR, Penumakala PK (2020) Bearing Fault Severity Analysis on A Multi-stage Gearbox Subjected to Fluctuating Speeds. Exp Tech 44(5):541–552
Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70–79
Ahamed N, Pandya Y, Parey A (2014) Spur gear tooth root crack detection using time synchronous averaging under fluctuating speed. Measurement 52:1–11
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VI performed the experiments and wrote the manuscript; SN assisted with ANFIS analysis; SGR guided the experiments and analysed the results. All authors read and approved the final version of the manuscript.
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Inturi, V., Shreyas, N. & Sabareesh, G.R. Anfis-Based Defect Severity Prediction on a Multi-Stage Gearbox Operating Under Fluctuating Speeds. Neural Process Lett 53, 3445–3466 (2021). https://doi.org/10.1007/s11063-021-10557-z
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DOI: https://doi.org/10.1007/s11063-021-10557-z