A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

Chih-Wen Chang, Hau-Wei Lee, Chein-Hung Liu
2018 Inventions  
This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A
more » ... m of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results. Inventions 2018, 3, 41 2 of 28 state automata [4], the continuous time RNN approach to dynamical systems [5], the RNN scheme for long short-term memory (LSTM) [6,7], the echo state network (ESN) approach to RNN training [8], the RNN algorithm for the learning of precise timing [9], the RNN encoder-decoder for learning phrase representations [10], the gated RNN method for sequence modeling [11], an overview of deep learning (DL) methods [12], the RNN scheme for machine health monitoring [13], machine health monitoring using convolutional bi-directional LSTM networks [14], the convolutional neural networks (CNN) method for handwritten digit recognition [15], the gradient-based learning approach to document recognition [16], the CNN scheme for object recognition [17], the CNN algorithm for large-scale hierarchical image databases [18], the CNN method for house number digit classification [19], the deep CNN approach to Imagenet classification [20], a CNN scheme for a hybrid nn-hmm model for speech recognition [21], the CNN approach to sentence classification [22], a deep residual learning algorithm for image recognition [23], the DL method for imbalanced multimedia data classification [24], a region-based CNN scheme for real-time object detection [25], a deep CNN based regression scheme for the estimation of remaining useful life [26], a deep CNN approach to automated feature extraction in industrial inspection [27], a CNN method for imbalanced classification [28], a deep neural networks (DNN) algorithm for natural language processing [29], a t-stochastic neighbor embedding method (t-SNE) for the visualization of high-dimensional data [30], a DNN method for acoustic modeling in speech recognition [31], a DNN approach to deep visualization [32], a DNN scheme for fault diagnosis [33], a restricted Boltzmann machine (RBM) method for failure diagnosis [34], an RBM approach to the regularization of prognosis and health assessment [35], an RBM scheme for the estimation of remaining useful life [36], a fast learning algorithm for deep belief nets [37], a deep multi-layer NNs for deep architecture [38], a deep Boltzmann machine (DBM) method for three-dimensional (3-D) object recognition [39], the introduction of a sparse auto-encoder learning algorithm [40], a review and new perspectives of representative learning methods [41], the introduction of extreme learning machine (ELM) methods [42], a deep auto-encoder (AE) approach to anomaly detection and fault disambiguation in large flight data [43], fault diagnosis using a denoising stacked auto-encoder [44], a continuous sparse auto-encoder (CSAE) approach to transformer fault diagnosis [45], a survey of transfer learning methods [46], the DL approach to tissue-regulated splicing code [47], the methods and applications of DL algorithms [48], the introduction of DL methods [49], a survey of the application of DL to machine health monitoring [50], an overview of DL approaches [51], an introduction to the learning of multiple layers of representation [52], a denoising AE for the extraction and composition of robust features [53], a large-scale deep unsupervised learning (UL) scheme for graphics processors and the building of high-level features [54,55], unsupervised learning of video representations using LSTM [56], the introduction of a constructive meta-learning (ML) method [57], an ML approach to automatic kernel selection [58], the ML method and search technique used to select parameters [59], an ML approach to the Bayesian optimization of hyperparameters [60], a clustering algorithm for new distance-based problems [61], Taxonomy and empirical analysis in a supervised learning (SL) scheme [62], a weakly SL algorithm and high-level feature learning for object detection in remotely sensed optical images [63], the introduction of an off-policy reinforcement learning (RL) method [64], a deep RL method for the augmentation of these models to exploit game feature information [65], a deep RL algorithm for robots to be learned directly from camera inputs in the real world [66], and the introduction of Q-learning approach [67]. Besides, our motivation is to organize and analyze those literature and find out the future research of smart machine tools. Detailed descriptions of these methods follow in Section 2. Many studies have been made on the diagnosis and detection of mechanical components over the last few decades: a neural network (NN) algorithm for motor rolling bearing fault diagnosis [68], the artificial neural network (ANN) method for rolling element bearing fault diagnosis [69], the Pca-based feature selection scheme for machine defect classification [70], the support vector machine (SVM) approach to machine condition monitoring and fault diagnosis [71], the NN method for induction machine condition monitoring [72], the ANN approach and SVM scheme for fault detection Inventions 2018, 3, 41 3 of 28 in ball-bearings [73], the stacked auto-encoder (SAE) and softmax regression approach for bearing fault diagnosis [74], the wavelet transform and SAE method for roller bearing fault diagnosis [75], the DNN scheme for rolling bearing fault diagnosis [76], a transfer learning-based approach for bearing fault diagnosis [77], the DNN for fault characteristic mining and the intelligent diagnosis of rotating machinery with massive data [78], a rapid Fourier transform (STFT)-deep learning scheme for rolling bearing fault diagnosis [79], a new bearing condition recognition method based on multi-feature extraction and DNN for intelligent bearing condition monitoring [80], an SVM and ANN approach to bearing health [81], the Weibull distribution and deep belief network (DBN) method for the assessment of bearing degradation [82], the multivibration signals and DBN scheme for bearing fault diagnosis [83], a hierarchical diagnosis network (HDN) approach for fault pattern recognition in rolling element bearings [84], an unsupervised feature extraction scheme for the diagnosis of journal bearing rotor systems [85], a CNN method for fault detection in rotating machinery [86], a hierarchical adaptive deep CNN approach to bearing fault diagnosis [87], a stacked denoising auto-encoder (SDA) method for fault diagnosis in rotary machine components [88], a sparse auto-encoder and DBN scheme for bearing fault diagnosis [89], a wavelet packet energy (WPE) image and deep convolutional network (ConvNet) for spindle bearing fault diagnosis [90], an extreme learning machine for online sequential prediction of bearing imbalance fault diagnosis [91], an ANN scheme for intelligent condition monitoring of gearboxes [92], an intelligent fault diagnosis and prognosis approach to rotating machinery that integrates wavelet transformation and principal component analysis [93], a multimodal deep support vector classification (MDSVC) approach to gearbox fault diagnosis [94], a multi-layer NN scheme for gearbox fault diagnosis [95], a CNN method for gearbox fault identification [96], a model for deep statistical feature learning from vibration measurements of rotating machines for fault diagnosis [97], a deep random forest fusion (DRFF) technique for gearbox fault diagnosis [98], a transfer component analysis (TCA) for gearbox fault diagnosis [99], a DBN method for structural health diagnosis [100], a DBN algorithm based state classification for engineering health diagnosis applications [101], a DL method for signal recognition and the diagnosis of spacecraft [102], long short-term memory neural network (LSTMNN) for fault diagnosis and estimation of the remaining useful life of aero engines [103], a physics-based approach to the diagnosis and prognosis of cracked rotor shafts [104], a multi-scale CNN scheme for rotor systems [105], a new support vector data description method for machinery fault diagnosis [106], a sparse auto-encoder-based DNN approach to induction motor fault classification [107], a DBN-based approach to induction motor fault diagnosis [108], a CNN method for real-time motor fault detection [109], an ANN and fuzzy logic (FL) scheme for the intelligent diagnosis of turbine blade faults [110], an analog-circuit fault diagnostic system based on the back propagation of neural networks using wavelet decomposition, principal component analysis, and data normalization [111], a logistic regression based prognostic method for the on-line assessment and classification of degradation and failure modes [112], naïve Bayes and Bayes net classifier algorithms for fault diagnosis in monoblock centrifugal pumps [113], sparse auto-encoders for the monitoring of rotating machines [114], an SAE for fault diagnosis in hydraulic pumps [115], a probabilistic kernel factor analysis (PKFA) method for the prediction of tool condition [116], a DNN approach to the diagnosis of tidal turbine vibration data [117], diagnosis methods based on deep learning for the early detection of small faults in front-end controlled wind generators (FSCWG) [118], and the CNN approach to real-time vibration-based structural damage detection [119]. Details of these methods are given in Section 3. There are many AI technique applications for smart machine tools: the ANN scheme for intelligent manufacturing [120], an integrated AI computer-aided process planning system [121], the application of AI to manufacturing systems [122], an NN analyzer for the mechanical properties of rolled steel bar [123], integrated artificial intelligent techniques for shape rolling sequences [124], the DBN method to the cutting state monitoring [125], multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing [126], survey control for intelligent manufacturing [127], a review of AI in intelligent manufacturing [128], an ANN scheme and fuzzy modeling system
doi:10.3390/inventions3030041 fatcat:6qrwhmrl2bfwrgmovqvsyx5p3y