Start-up vibration analysis for novelty detection on industrial gas turbines

This paper focuses on industrial application of start-up vibration signature analysis for novelty detection with experimental trials on industrial gas turbines (IGTs). Firstly, a representative vibration signature is extracted from healthy start-up vibration measurements through the use of an adaptive neuro-fuzzy inference system (ANFIS). Then, the first critical speed and the vibration level at the critical speed are located from the signature. Finally, two (s- and v-) health indices are introduced to detect and identify different novel/fault conditions from the IGT start-ups, in addition to traditional similarity measures, such as Euclidean distance and cross-correlation measures. Through a case study on IGTs, it is shown that the presented approach provides a convenient and efficient tool for IGT condition monitoring using start-up field data

Formulation of Fuzzy Correlated System for Node Behavior Detection in WSN

Wireless Sensor Network depends highly upon the cooperation among the nodes behavior in transmission of packet data, messages and route discovery. Over open medium environment, nodes are free to move and may change their behavior arbitrarily. In the presence of misbehavior node in some cases, it may instigate its neighboring nodes to compromise with the misbehaved node. Thus, this has resulted to a spreading of correlated node behavior and the impact of this event may result in high severity in network performance. Therefore, fuzzy logic model is proposed to formulate the correlated node behavior in WSN. The formulation of correlated node behavior based on fuzzy logic function of peer nodes real parameter measurement is investigated to determine the status of the node and then the fuzzy neural network will model the correlated node behavior occurrence. The accuracy of the results is established via sensor network simulation. The result of this study is providing a fundamental guideline for network designer in order to understand the fault-tolerance in network topology

Estimating gas turbine compressor discharge temperature using Bayesian neuro-fuzzy modelling

The objective of this paper is to estimate the compressor discharge temperature measurements on an industrial gas turbine that is undergoing commissioning at site, using a data-driven model which is built using the test bed measurements of the engine. This paper proposes a Bayesian neuro-fuzzy modelling (BNFM) approach, which combines the adaptive neuro-fuzzy inference system (ANFIS) and variational Bayesian Gaussian mixture model (VBGMM) techniques. A data-driven compressor model is built using ANFIS, and VBGMM is applied in the set-up stage to automatically select the number of input membership functions in the fuzzy system. The efficacy of the proposed BFNM approach is established through experimental trials of a sub-15MW gas turbine, and the results, from the model that is built using test bed data, are shown to be promising for estimating the compressor discharge temperatures on the gas turbine during commissioning

EFFECTS OF MONITORING SIGNAL HYSTERESIS ON SPEED REGULATION FOR THE AERO-DERIVATIVE GAS TURBINE

Sensor aging and sensor failure are the common phenomena due to the high temperature and pressure environment for gas turbines, which can lead to hysteresis of monitoring signals. In this paper, a kind of aero-derivative gas turbine is taken as the research object. The hysteresis effects of single monitoring signal and coupling of multiple monitoring signals on speed control are mainly studied, and the analysis is carried out from the perspective of adjustment time, overshoot, fuel quantity and fuel quantity regulation output. The analysis results show that the pressure signal hysteresis will lead to speed suspension. The speed signal hysteresis will change the speed regulation into a multi-step mode. When the monitoring signal hysteresis is coupled, the effect of pressure signal hysteresis is greater than that of speed signal hysteresis. The results of this paper can provide a reference for the optimal design of speed control of aero-derivative gas turbine

Identification of Transverse Crack in a Cracked Cantilever Beam Using Fuzzy Logic and Kohonen Network

The issue of crack detection and diagnosis has gained wide spread industrial interest. Crack/damage affects the industrial economic growth. Generally damage in a structural element may occur due to normal operations, accidents, deterioration or severe natural events such as earth quake or storms. Damage can be analyzed through visual inspection or by the method of measuring frequency, mode shape and structural damping. Damage detection by visual inspection is a time consuming method and measuring of mode shape as well as structural deflection is difficult rather than measuring frequency. As Non- destructive method for the detection of crack is favorable as compared to destructive methods. So, our analysis has been made on the basis of non-destructive methods with the consideration of natural frequency. Here the crack is transverse surface crack. In the current analysis, methodologies have been developed for damage detection of a cracked cantilever beam using analytical, fuzzy logic, kohonen network as well as experimental. Theoretical analysis has been carried out to calculate the natural frequency with the consideration of mass and stiffness matrices. The data obtained from theoretical analysis has been fed to fuzzy controller as well as the kohonen competitive learning network. The Fuzzy Controller uses the different membership functions as input as well as output. The input parameters to the Fuzzy Controller are the first three natural frequencies. The output parameters of the fuzzy controller are the relative crack depth and relative crack location. Several Fuzzy rules have been trained to obtain the results for relative crack depth and relative crack location. Kohonen network is nothing but a competitive learning network is used here for the detection of crack depth and location. It is processed through a vector quantization algorithm. A comparative study has been made between fuzzy logic technique and Kohonen network technique after experimental verification. It has been observed that the process of kohonen network can predict the depth and location accurately as close to fuzzy logic technique

Failure Diagnosis and Prognosis of Safety Critical Systems: Applications in Aerospace Industries

Many safety-critical systems such as aircraft, space crafts, and large power plants are required to operate in a reliable and efficient working condition without any performance degradation. As a result, fault diagnosis and prognosis (FDP) is a research topic of great interest in these systems. FDP systems attempt to use historical and current data of a system, which are collected from various measurements to detect faults, diagnose the types of possible failures, predict and manage failures in advance. This thesis deals with FDP of safety-critical systems. For this purpose, two critical systems including a multifunctional spoiler (MFS) and hydro-control value system are considered, and some challenging issues from the FDP are investigated. This research work consists of three general directions, i.e., monitoring, failure diagnosis, and prognosis. The proposed FDP methods are based on data-driven and model-based approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the remaining useful life (RUL) of the faulty components accurately and efficiently. In this regard, two dierent methods are developed. A modular FDP method based on a divide and conquer strategy is presented for the MFS system. The modular structure contains three components:1) fault diagnosis unit, 2) failure parameter estimation unit and 3) RUL unit. The fault diagnosis unit identifies types of faults based on an integration of neural network (NN) method and discrete wavelet transform (DWT) technique. Failure parameter estimation unit observes the failure parameter via a distributed neural network. Afterward, the RUL of the system is predicted by an adaptive Bayesian method. In another work, an innovative data-driven FDP method is developed for hydro-control valve systems. The idea is to use redundancy in multi-sensor data information and enhance the performance of the FDP system. Therefore, a combination of a feature selection method and support vector machine (SVM) method is applied to select proper sensors for monitoring of the hydro-valve system and isolate types of fault. Then, adaptive neuro-fuzzy inference systems (ANFIS) method is used to estimate the failure path. Similarly, an online Bayesian algorithm is implemented for forecasting RUL. Model-based methods employ high-delity physics-based model of a system for prognosis task. In this thesis, a novel model-based approach based on an integrated extended Kalman lter (EKF) and Bayesian method is introduced for the MFS system. To monitor the MFS system, a residual estimation method using EKF is performed to capture the progress of the failure. Later, a transformation is utilized to obtain a new measure to estimate the degradation path (DP). Moreover, the recursive Bayesian algorithm is invoked to predict the RUL. Finally, relative accuracy (RA) measure is utilized to assess the performance of the proposed methods

Multiple Damage Identification of Beam Structure Using Vibration Analysis and Artificial Intelligence Techniques

This thesis investigates the problem of multiple damage detection in vibrating structural members using the dynamic response of the system. Changes in the loading patterns, weakening/degeneration of structures with time and influence of environment may cause cracks in the structure, especially in engineering structures which are developed for prolonged life. Hence, early detection of presence of damage can prevent the catastrophic failure of the structures by appropriately monitoring the response of the system. In recent times, condition monitoring of structural systems have attracted scientists and researchers to develop on line damage diagnostic tool. Primarily, the structural health monitoring technique utilizes the methodology for damage assessment using the monitored vibration parameters. In the current analysis, special attention has been focused on those methods capable of detecting multiple cracks present in system by comparing the information for damaged and undamaged state of the structure. In the current research, methodologies have been developed for damage detection of a cracked cantilever beam with multiple cracks using analytical, Finite Element Analysis (FEA), fuzzy logic, neural network, fuzzy neuro, MANFIS, Genetic Algorithm and hybrid techniques such as GA-fuzzy, GA-neural, GA-neuro- fuzzy. Analytical study has been performed on the cantilever beam with multiple cracks to obtain the vibration characteristics of the beam member by using the expressions of strain energy release rate and stress intensity factor. The presence of cracks in a structural member introduces local flexibility that affects its dynamic response. The local stiffness matrices have been measured using the inverse of local dimensionless compliance matrix for finding out the deviation in the vibrating signatures of the cracked cantilever beam from that of the intact beam. Finite Element Analysis has been carried out to derive the vibration indices of the cracked structure using the overall flexibility matrix, total flexibility matrix, flexibility matrix of the intact beam. From the research done here, it is concluded that the performance of the damage assessment methods depends on several factors for example, the number of cracks, the number of sensors used for acquiring the dynamic response, location and severity of damages. Different artificial intelligent model based on fuzzy logic, neural network, genetic algorithm, MANFIS and hybrid techniques have been designed using the computed vibration signatures for multiple crack diagnosis in cantilever beam structures with higher accuracy and considerably low computational time

Dynamic Analysis of Cracked Rotor in Viscous Medium and its Crack Diagnosis Using Intelligent Techniques

Fatigue cracks have high potential to cause catastrophic failures in the rotor which can lead to catastrophic failure if undetected properly and in time. This fault may interrupt smooth, effective and efficient operation and performance of the machines. Thereby the importance of identification of crack in the rotor is not only for leading safe operation but also to prevent the loss of economy and lives.The condition monitoring of the engineering systems is attracted by the researchers and scientists very much to invent the automated fault diagnosis mechanism using the change in dynamic response before and after damage. When the rotor with transverse crack immersed in the viscous fluid, analysis of cracked rotor is difficult and complex. The analysis of cracked rotor partially submerged in the viscous fluid is widely used in various engineering systems such as long spinning shaft used drilling the seabed for the extracting the oil, high-speed turbine rotors, and analysis of centrifuges in a fluid medium. Therefore, dynamic analysis of cracked rotor partially submerged in the viscous medium have been presented in the current study. The theoretical analysis has been performed to measure the vibration signatures (Natural Frequencies and Amplitude) of multiple cracked mild steel rotor partially submerged in the viscous medium. The presence of the crack in rotor generates an additional flexibility. That is evaluated by strain energy release rate given by linear fracture mechanics. The additional flexibility alters the dynamic characteristics of cracked rotor in a viscous fluid. The local stiffness matrix has been calculated by the inverse of local dimensionless compliance matrix. The finite element analysis has been carried out to measure the vibration characteristics of cracked rotor partially submerged in the viscous medium using commercially available finite element software package ANSYS. It is observed from the current analysis, the various factors such as the viscosity of fluid, depth and position of the cracks affect the performance of the rotor and effectiveness of crack detection techniques. Various Artificial Intelligent (AI) techniques such as fuzzy logic, hybrid BPNN-RBFNN neural network, MANFIS and hybrid fuzzy-rule base controller based multiple faults diagnosis systems are developed using the dynamic response of rotating cracked rotor in a viscous medium to monitor the presence of crack. Experiments have been conducted to authenticate the performance and accuracy of proposed methods. Good agreement is observed between the results

Fault diagnosis in industrial process by using LSTM and an elastic net

[EN] Fault diagnosis is important for industrial processes because it permits to determine the necessity of emergency stops in a process and/or to propose a maintenance plan. Two strategies for fault diagnosis are compared in this work. On the one hand, the data are preprocessed using the independent components analysis for dimension reduction, then the wavelet transform is used in order to highlight the faulty signals, with this information an artificial neural network was fed. On the other hand, the second strategy, the main contribution of this work, is the implementation of a long short term memory. This memory is fed with the most representative variables selected by an elastic net to use both, the L1 and L2 norms. These strategies are applied in the Tennessee Eastman process, a benchmark widely used for fault diagnosis. The fault isolation had better results than those reported in the literature.[ES] El diagnóstico de fallas es importante en los procesos industriales, ya que permite determinar si es necesario detener el proceso en operación y/o proponer un plan de mantenimiento. En el presente trabajo se comparan dos estrategias para diagnosticar fallas. La primera realiza un preprocesamiento de datos usando el análisis de componentes independientes para reducir la dimensión de los datos, posteriormente, se emplea la transformada wavelet para resaltar las señales de falla, con esta información se alimenta una red neuronal artificial. Por su parte, la segunda estrategia, principal contribución de este trabajo, usa una memoria de corto y largo plazo. Esta memoria es alimentada por las variables más significativas seleccionadas mediante una red elástica para usar tanto la norma $L_1$ como la $L_2$. Como ejemplo de aplicación se utilizó el proceso químico Tennessee Eastman, un proceso ampliamente usado en el diagnóstico de fallas. El aislamiento de fallas mostró mejores resultados con respecto a los reportados en la literatura.Márquez-Vera, MA.; López-Ortega, O.; Ramos-Velasco, LE.; Ortega-Mendoza, RM.; Fernández-Neri, BJ.; Zúñiga-Peña, NS. (2021). Diagnóstico de fallas mediante una LSTM y una red elástica. Revista Iberoamericana de Automática e Informática industrial. 18(2):164-175. https://doi.org/10.4995/riai.2020.13611OJS164175182Adewole, A., Tzoneva, R., Behardien, S., 2016. Distribution network fault section identification and fault location using wavelet entropy and neural networks. Applied Soft Computing 46, 296-306. https://doi.org/10.1016/j.asoc.2016.05.013Alkaya, A., Eker, I., 2011. Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application. ISA Transactions 50, 287-302. https://doi.org/10.1016/j.isatra.2010.12.004Barakat, S., Eteiba, M., Wahba, W., 2014. Fault location in underground cables using anfis nets and discrete wavelet transform. Journal of Electrical Systems and Information Technology 1, 198-211. https://doi.org/10.1016/j.jesit.2014.12.003Bathelt, A., Ricker, N., Jelali, M., 2015. Revision of the Tennessee Eastman process model. IFAC Papers-Online 48 (8), 309-314. https://doi.org/10.1016/j.ifacol.2015.08.199Boldt, F., Rauber, T., Varejao, F., October 2014. Evaluation of the extreme learning machine for automatic fault diagnosis of the Tennessee Eastman chemical process. In: IEEE (Ed.), Annual Conference of the IEEE Industrial Electronics Society. Vol. 40. Dallas, Texas, pp. 2551-2557. https://doi.org/10.1109/IECON.2014.7048865Chen, H., Tino, P., Yao, X., 2014. Cognitive fault diagnosis in Tennessee Eastman process using learning in the model space. Computers and Chemical Engineering 67, 33-42. https://doi.org/10.1016/j.compchemeng.2014.03.015Rodrigues, J., Filho, P., PeixotoJr., E., Kumar, A., deAlbuquerque, V., 2019. Classification of EEG signals to detect alcoholism using machine learning techniques. Pattern Recognition Letters 125, 140-149. https://doi.org/10.1016/j.patrec.2019.04.019Dixit, A., Majumdar, S., 2013. Comparative analysis of coiflet and daubechies wavelets using global threshold for image denoising. Intenational Journal of Advances in Engineering & Technology 6 (5), 2247-2252.Downs, J., Vogel, E., 1993. A plant-wide industrial process control problem. Computers and Chemical Engineering 17 (3), 245-255. https://doi.org/10.1016/0098-1354(93)80018-IFischer, T., Krauss, C., 2018. Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270, 654-669. https://doi.org/10.1016/j.ejor.2017.11.054Gao, X., Hou, J., 2016. An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process. Neurocomputing 174, 906-911. https://doi.org/10.1016/j.neucom.2015.10.018Geng, Z., Li, Z., Han, Y., 2018. A new deep belief network based on RBM with glial chains. Information Sciences 463, 294-306. https://doi.org/10.1016/j.ins.2018.06.043Goodfellow, I., Bengio, Y., Courville, A., 2016. Deep Learning. MIT Press, United States of America, http://www.deeplearningbook.ogr.Han, L., Li, C., Guo, S., Su, X., 2015. Feature extraction method of bearing AE signal based on improved Fast-ICA and wavelet packet energy. Mechanical Systems and Signal Processing 62-63, 91-99. https://doi.org/10.1016/j.ymssp.2015.03.009Hastie, T., Tibshirani, R., Friedman, J., 2009. The elements of statistical learning: data mining, inference and prediction. Springer, New York. https://doi.org/10.1007/978-0-387-84858-7Hoang, D., Kang, H., 2019. A survey on deep learning based bearing fault diagnosis. Neurocomputing 335, 327-335. ttps://doi.org/10.1016/j.neucom.2018.06.078Hochreiter, S., Schmidhuber, J., 1997. Long short term memory. Neural Computation 9 (8), 1735-1780. ttps://doi.org/10.1162/neco.1997.9.8.1735Hyvärinen, A., Oja, E., 2000. Independent component analysis: Algorithms and applications. Neural Networks 13, 411-430. ttps://doi.org/10.1016/S0893-6080(00)00026-5Jing, C., Gao, X., Zhu, X., Lang, S., July 2014. Fault classificaction on Tennessee Eastman process: PCA and SVM. In: IEEE (Ed.), Intenational Conference on Mecatronics and Control. Jinzhou, China, pp. 2194-2197. https://doi.org/10.1109/ICMC.2014.7231958Jung, C., Kim, K., Lee, J., Klockl, B., 2007. Wavelet and neuro-fuzzy based fault location for combined transmission systems. Energy Systems 29, 445-454. https://doi.org/10.1016/j.ijepes.2006.11.003Kandula, V. K., 2011. Fault detection in process control plants using principal component analysis. Master's thesis, Louisiana State University, Department of Electrical Engineering.Karpenko, M., Sepehri, N., Octubre 2001. A neural network based fault detection and identification scheme for pneumatic process control valves. In: IEEE (Ed.), International Conference on Systems, Man and Cybernetics. Tucson, USA, pp. 93-98. https://doi.org/10.1109/ICSMC.2001.969794Khakipour, M., Safavi, A., Setoodeh, P., 2017. Bearing fault diagnosis with morphological gradient wavelet. Journal of the Franklin Institute 354, 2465-2476. https://doi.org/10.1016/j.jfranklin.2016.11.013Kuang, T., Yang, Z., Yao, Y., 2015. Multivariate fault isolation via variable selection in discriminant analysis. Journal of Process Control 35, 30-40. https://doi.org/10.1016/j.isatra.2017.06.014Kumar, R., Bansal, H., 2019. Hardware in the loop implementation of wavelet based strategy in shuntactive powerfilter to mitigate power quality issues. Electric Power Systems Research 169, 92-104. https://doi.org/10.1016/j.epsr.2019.01.001Lau, C., Ghosh, K., Hussain, M., Hassan, C. C., 2013. Fault disgnosis of Tennessee Eastman process with multi-scale PCA and ANFIS. Chemom. Intell. Lab. Syst. 120, 1-14. https://doi.org/10.1016/j.chemolab.2012.10.005Lee, J., Yoo, C., Lee, I., 2004. Statistical process monitoring with independent component analysis. Journal of Process Control 14 (5), 467-485. https://doi.org/10.1016/j.jprocont.2003.09.004Lei, J., Liu, C., Jiang, D., 2019. Fault diagnosis of wind turbine based on long short-term memory networks. Renewable Energy 133, 422-432. https://doi.org/10.1016/j.renene.2018.10.031Li, W., Monti, A., Ponci, F., 2014. Fault detection and classification in medium voltage DC shipboard power systems with wavelets and artificial neural networks. IEEE Transactions on Instrumentation and Measurement 63 (11), 2651-2665. https://doi.org/10.1109/TIM.2014.2313035Liang, P., Deng, C.,Wu, J., Yang, Z., Zhu, J., Zhang, Z., 2019. Compound fault diagnosis of gearboxes via multi-label convolutional neural network and wavelet transform. Computers in Industry 113, 103132. https://doi.org/10.1016/j.compind.2019.103132Lin, J., Zhang, A., 2005. Fault feature separation using wavelet-ICA filter. NDT&E International 38, 421-427. https://doi.org/10.1016/j.ndteint.2004.11.005Linker, R., Gutman, P., Seginer, I., 2002. Observer-based robust failure detection and isolation in greenhouses. Control Engineering Practice 10 (5), 519- 531. https://doi.org/10.1016/S0967-0661(02)00002-3Lou, W., Loparo, K., 2004. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing 18, 1077-1095. https://doi.org/10.1016/S0888-3270(03)00077-3Lv, F.,Wen, C., Bao, Z., Liu, M., 2016. Fault diagnosis based on deep learning. In: AACC (Ed.), American Control Conference. Boston, USA, pp. 6851-6856. https://doi.org/10.1109/ACC.2016.7526751Lv, F., Wen, C., Liu, M., Bao, Z., 2017. Weighted time series fault diagnosis based on a staked sparce autoencoder. Journal of Chemometrics 31, 16 pages. https://doi.org/10.1002/cem.2912Lv, F., Fan, X., Wen, C., Bao, Z., 2018. Stacked sparse auto encoder network based multimode process monitoring. In: IEEE (Ed.), International Conference on Control Automation & Information Science. Hangzhou, China, pp. 227-232. https://doi.org/10.1109/ICCAIS.2018.8570618Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., Strintzis, M., 1998. ECG pattern recognition and classification using non-linear transfor mations and neural networks: A review. International Journal of Medical Informatics 52, 191-208. https://doi.org/10.1016/S1386-5056(98)00138-5Methnani, S., Lafont, F., Gautier, J., Damak, T., Toumi, A., 2013. Actuator and sensor fault detection, isolation and identification in nonlinear dynamical systems, with applications to a waste water treatment plant. Journal of Computer Engineering and Informatics 1 (4), 112-125. https://doi.org/10.1080/21642583.2014.888525Muñoz-Cobo, J., Mendizábal, R., Miquel, A., Berna, C., Escrivá, A., 2017. Use of the principles of maximum entropy and maximum relative entropy for the determination of uncertain parameter distributions in engineering applications. Entropy 19, 486, 37 pages. https://doi.org/10.3390/e19090486Nguyen, B., Quyen, A., Nguyen, P., Ton, T., July 2017. Wavelet-based neural network for recognition of faults at nhabe power substation of the vietnam power system. In: IEEE (Ed.), International Conference on System Science and Engineering. Ho Chi Minh City, Vietnam, pp. 165-168. https://doi.org/10.1109/ICSSE.2017.8030858Ojeda-González, A., Mendes-Jr., O., Oliveira-Domingues, M., Menconi, V., 2014. Daubechies wavelet coeffcients: a tool to study interplanetary magnetic field fluctuations. Geof'ısica Internacional 53 (2), 101-115. https://doi.org/10.1016/S0016-7169(14)71494-1Oliveira, J., Pontes, K., Santori, I., Embirucu, M., 2017. Fault detection and diagnosis in dynamic systems using weightless neural networks. Expert Systems With Applications 84, 200-219. https://doi.org/10.1016/j.eswa.2017.05.020Patan, K., 2008. Artificial neural networks for the modelling and fault diagnosis of technical process. Lecture Notes in Control and Information Sciences. Springer, India.Rafiee, J., Rafiee, M., Tse, P., 2010. Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications 37, 4568-4579. https://doi.org/10.1016/j.eswa.2009.12.051Ramos-Velasco, L., Ramos-Fernández, J., Islar-Gómez, O., Espejel-Rivera, M., García-Lamont, J., Márquez-Vera, M., 2013. Identificación y control wavenet de un motor de ca. Revista Iberoamericana de Automática e Informática Industrial 10, 269-278. https://doi.org/10.1016/j.riai.2013.05.002Rato, T., Reis, M., 2013. Defining the structure of DPCA models and its impact on process monitoring and prediction ctivities. Chemometrics and Intelligent Laboratory Systems 125, 74-86. https://doi.org/10.1016/j.chemolab.2013.03.009Rockinger, M., Jondeau, E., 2002. Entropy densities with an application to autoregressive conditional skewness and kurtosis. Journal of Econometrics 106, 119-142. https://doi.org/10.1016/S0304-4076(01)00092-6Salahschoor, K., Kiasi, F., July 2008. On-line process monitoring based on wavelet-ICA methodology. In: IFAC (Ed.), Proceedings of the 17th World Congress. Seul- Korea, pp. 6-11. https://doi.org/10.3182/20080706-5-KR-1001.01253Salahshoor, K., Khoshro, M., Kordestani, M., 2011. Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems. Simulation Modelling Practice and Theory 19, 1280-1293. https://doi.org/10.1016/j.simpat.2011.01.005Sharif, I., Khare, S., 2014. Comparative analysis of Haar and Daubechies wavelet for hyper spectral image classification. In: Commission, I. T. (Ed.), VIII Symposium of The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science. Hyderabad-India, pp. 937-941. https://doi.org/10.5194/isprsarchives-XL-8-937-2014Smirnov, E., Timoshenko, D., Adrianov, S., 2014. Comparison of regularization methods for imagenet classification with deep convolutional neural networks. AASRI Procedia 6, 89-94. https://doi.org/10.1016/j.aasri.2014.05.013Sobhani-Tehrani, E., Khorasani, K., 2009. Fault diagnosis of nonlinear systems using a hybrid approach. Fault detetion and diagnosis. Springer, Berlin, Ch. 2, pp. 22-49. https://doi.org/10.1007/978-0-387-92907-1_2Tayarani-Bathaie, S., Vanini, Z., Khorasani, K., 2014. Dynamic neural networkbased fault diagnosis of gas turbine engines. Neurocomputing 125, 153-165. https://doi.org/10.1016/j.neucom.2012.06.050Zvokelj, M., Zupan, S., Prebil, I., 2016. EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. Journal of Sound and Vibration 26, 394-423. https://doi.org/10.1016/j.jsv.2016.01.046Wang, X., Qin, Y., Wang, Y., Xiang, S., Chen, H., 2019. ReLTanh: An activation function with vanishing gradient resistance for SAE-based DNNs and its application to rotating machinery fault diagnosis. Neurocomputing 363, 88-98. https://doi.org/10.1016/j.neucom.2019.07.017Wu, F., Tong, F., Yang, Z., 2016. EMGdi signal enhancement based on ICA decomposition and wavelet transform. Applied Soft Computing 43, 561-571. https://doi.org/10.1016/j.asoc.2016.03.002Wu, J., Hsu, C., Wu, G., 2009. Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference. Expert Systems with Applications 36, 6244-6255. https://doi.org/10.1016/j.eswa.2008.07.023Wu, Q., Law, R., Wu, S., 2011. Fault diagnosis of car assembly line based on fuzzy wavelet kernel support vector classifier machine and modified genetic algorithm. Expert Systems with Applications 38, 9096-9104. https://doi.org/10.1016/j.eswa.2010.12.109Wu, H., Zhao., Jinsong, 2018. Deep convolutional neural network model based chemical process fault diagnosis. Computers and Chemical Engineering 115, 185-197. https://doi.org/10.1016/j.compchemeng.2018.04.009Xiao, C., Chen, N., Hu, C., Wang, K., Gong, J., Chen, Z., 2019. Short and midterm sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach. Remote Sensing of Environment 233, 111358. https://doi.org/10.1016/j.rse.2019.111358Xie, D., Bai, L., December 2015. A hierarchical deep neural network for fault diagnosis on Tennessee-Eastman process. In: IEEE (Ed.), International Conference on Machine Learning and Applications. Vol. 14. Miami, USA, pp. 745-748. https://doi.org/10.1109/ICMLA.2015.208Yan, R., Gao, R., Chen, X., 2014. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing 351, 4555-4569. https://doi.org/10.1016/j.sigpro.2013.04.015Yan, Z., Yao, Y., 2015. Variable selection method for fault isolation using least absolute shrinkage and selection operator (LASSO). Chemometrics and Intelligent Laboratory Systems 146, 136-146. https://doi.org/10.1016/j.chemolab.2015.05.019Yao, G., Lei, T., Zhong, J., 2019. A review of convolutional-neural-networkbased action recognition. Pattern Recognition Letters 118, 14-22. https://doi.org/10.1016/j.patrec.2018.05.018Yin, S., Ding, S., Haghani, A., Hao, H., Zhang, P., 2012. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. Journal of Process Control 22, 1567-1581. https://doi.org/10.1016/j.jprocont.2012.06.009Zhang, Q., Yang, L., Chen, Z., Li, P., 2018. A survey on deep learning for big data. Information Fusion 42, 146-157. https://doi.org/10.1016/j.inffus.2017.10.006Zhang, X., Polycarpou, M., Parisini, T., 2002. A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems. IEEE Transactions on Automatic Control 47 (4), 576-593. https://doi.org/10.1109/9.995036Zhang, Y., Zhang, L., Zhang, H., 2012. Fault detection for industrial processes. Mathematical Problems in Engineering 2012, 18 pages. https://doi.org/10.1155/2012/757828Zhang, Z., Zhao, J., 2017. A deep belief network based fault diagnosis model for complex chemical process. Computers and Chemical Engineering 107, 395-407. https://doi.org/10.1016/j.compchemeng.2017.02.041Zhao, H., 2018. Neural component analysis for fault detection. Chemometrics and Intelligent Laboratory Systems 176, 11-21. https://doi.org/10.1016/j.chemolab.2018.02.001Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., Gao, R., 2019. Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing 115, 213-237. https://doi.org/10.1016/j.ymssp.2018.05.050Zheng, J., Huang, W., Wang, Z., Liang, J., 2019. Mutual information-based sparse multiblock dissimilarity method for incipient fault detection and diagnosis in plant-wide process. Journal of Process Control 83, 63-76. https://doi.org/10.1016/j.jprocont.2019.09.00