Ensemble local kernel learning for online prediction of distributed product outputs in chemical processes

Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

“…However, traditional JIT soft sensors attempt to build a globally optimal encapsulation of local modeling techniques, similarity measures, input variables, model hyper-parameters, etc., while the diversity of JIT learning is ignored. To tackle this problem, various ensemble JIT learning (EJIT) soft sensors have been developed [6,8,25,[28][29][30][31]. e basic idea of EJIT modeling is to build multiple component JIT learners and then combine their predictions.…”

“…Kaneko and Funatsu (2016) [25] developed an ensemble locally weighted partial least squares (LWPLS) soft sensor, where diverse subsets are first built using moving window method and then multiple of the most relevant ones to the query state are selected to build diverse LWPLS models, which are finally integrated via Bayes' theorem. [29] built an EJIT kernel learning framework through perturbing the hyperparameters of local learning methods. Yuan et al (2018) [30] developed an EJIT soft sensor by using different similarity measures.…”

“…However, it remains challenging to build highperformance EJIT soft sensors due to the following issues. First, many current EJIT soft sensor methods only consider single perturbation, such as perturbing training data [25], similarity measure [30,31], perturbing input variables [8], perturbing local modeling technique [28], or perturbing model parameters [29]. In practice, the diversity of JIT learning is often originated from multiple factors.…”

A heterogeneous ensemble approach for the prediction of the remaining useful life of packaging industry machinery

“…Thus, the combination of the outcomes of the individual models in the ensemble improves the accuracy of the predictions compared to the performance of a single model (Brown et al, 2005) (Baraldi et al, 2013a). Different methods, such as ANN (Baraldi et al, 2013b), Support Vector Machine (SVM) (Liu et al, 2006) and kernel learning (Liu et al, 2015), have been used with success to build the individual models. For example, an ensemble of feedforward Artificial Neural Networks (ANN) has been embedded into a Particle Filter (PF) for the prediction of crack length evolution (Baraldi et al, 2013b) and an ensemble of datadriven regression models has been exploited for the RUL prediction of lithium-ion batteries (Xing et al, 2013).…”

Synergy of computers, cognition, communication and control with industrial applications

“…The synergy of CCCC supports the increasingly demanding performance specifications of these applications and helps to face special situations like unexpected condition adaptations, human interaction challenges, and goal conflicts. Practical industrial applications of the synergy of CCCC are cyber-physical systems [1][2][3][4][5], networked control systems [6][7][8][9][10], mechatronics systems [11][12][13][14][15], online quality control of production items [16][17][18][19][20], supervision and failure analysis of dynamically changing machine states [21][22][23][24], decision support systems [25][26][27][28][29], prediction and control in dynamic production processes [30][31][32], welding processes [33,34], user profiling [35,36], process monitoring [37][38][39], web based control of information management flows [40,41], and resilient control architectures [42][43][44]…”