Process controls (basic as well as advanced) are implemented within the process control system, which may mean a distributed control system (DCS), programmable logic controller (PLC), and/or a supervisory control computer. DCSs and PLCs are typically industrially hardened and fault-tolerant. Supervisory control computers are often not hardened or fault-tolerant, but they bring a higher level of computational capability to the control system, to host valuable, but not critical, advanced control applications. Advanced controls may reside in either the DCS or the supervisory computer, depending on the application. Basic controls reside in the DCS and its subsystems, including PLCs. Because we usually deal with real - world systems with real - world constraints (cost, computer resources, size, weight, power, heat dissipation, etc.), it is understood that the simplest method to accomplish a task is the one that should be used. Experts usually rely on common sense when they solve problems. They also use vague and ambiguous terms. Other experts have no difficulties with understanding and interpreting this statement because they have the background to hearing problems described like this. However, a knowledge engineer would have difficulties providing a computer with the same level of understanding. In a complex industrial process, how can we represent expert knowledge that uses vague and fuzzy terms in a computer to control it? In this context, the application is developed to control the pretreatment and pasteurization station of milk localized in Batna (Algeria) by adopting a control approach based on expert knowledge and fuzzy logic.

Prognostic and Health Management of multi-components systems is a challenging task, where different types of dependencies may exist. To tackle the complexity of such system, many distributed approaches are proposed. They deal with structural and functional dependencies and suppose that components are independent, which is not the case in reality. The transition of one component to more degraded states may affect other dependent components. In this paper we address the problem of prognosing components under stochastic dependence, we propose a prognostic based agent function in which a set of agents interact and coordinate to estimate the component remaining useful life with degradation interactions in a distributed manner. The simulation of an example taken from the literature is done on jade framework, the results indicate that the precision of the component RUL is enhanced.

In this article, we present ARPM (Augmented Reality-based Preventive Maintenance) system, which aims at offering to the technician of a Cement Factory a virtual assistance in real time using various forms of 3D models, images or even texts, in order to help him to maintain and inspect his equipment. This system not only offers the possibility of reducing costs of the preventive maintenance, but also allows monitoring and even anticipating failures. In order to ensure a reliable maintenance operation, we adapted our system to be compatible with the CMMS system (Computerized Maintenance Management System) already exists in the Cement factory

In this paper, we propose an algorithm for classification based on extreme learning machine, named TS-ELM (Two feature Spaces Extreme learning Machine). It is a hybridization of two distinct feature mapping of SLFNs (Single hidden layer feedforward neural network). Both are realized based on ELM theories. The first feature space resulted from a random kernel distributions of a randomly connected input samples, that inspired initially from conventional random connected neural network and it is also included in ELM theories. The second feature space that gives the training or testing samples is the result of a random probability distribution of fully connected samples taken from the first feature space, and adjusted with their own weight and threshold parameters. This algorithm was applied on three datasets of multiclass and binary classification, and the results shows that it has strong classification capabilities and universal approximation as well. The mechanisms of the algorithm and universal approximation are discussed in this paper.

Automatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system. In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural human-machine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.

The automation of production systems has been an
answer to the changing and competitive industrial context and
works by extracting data and experiences of experts. This
automation is a double-edged sword; on one hand, it increases
the productivity of the technical system (cost reduction,
reliability, availability, quality), but, on the other hand, it
increases the complexity of the system. This has led to the need
of efficient technologies, such as Supervisory Control and Data
Acquisition (SCADA) systems and techniques that could
absorb this complexity such as artificial intelligence and fuzzy
logic. In this context, we develop an application that controls
the pretreatment and pasteurization station of milk localized in
Batna (Algeria) by adopting a control approach based on
expert knowledge and fuzzy logic.

This paper illustrates an internet-based fuzzy control system of complex industrial manufacturing, which is cement production. It ensures remote and fuzzy control of the process in real time in cement factories in Algeria. The remote control system contains several tasks, such as alarms diagnostic, e-maintenance and synchronising regulation loops, to guarantee the automated performance. To evolve the system, we propose firstly, fuzzy logic to control the cement mill workshop and ensure that the system is operational with minimal downtime. Secondly, we integrate internet technology to remote control via internet to secure human life and render it unnecessary for operators to be at site. When there is a breakdown, it is not necessary to send an expert to diagnose and solve problems. Therefore, the system reduces travel costs by sending reports and transmitting process data. Operators can execute and monitor the system according to authentication access in main control room or via internet.

Fault prognosis in industrial plants is a complex problem, and time is an important factor for the resolution of this problem. The main indicator for the task of fault prognosis is the estimate of remaining useful life (RUL), which essentially depends on the predicted time to failure. This paper introduces a temporal neuro-fuzzy system (TNFS) for performing the fault prognosis task and exactly estimating the RUL of preheater cyclones in a cement plant. The main component of the TNFS is a set of temporal fuzzy rules that have been chosen for their ability to explain the behavior of the entire system, the components’ degradation, and the RUL estimation. The benefit of introducing time in the structure of fuzzy rules is that a local memory of the TNFS is created to capture the dynamics of the prognostic task. More precisely, the paper emphasizes improving the performance of TNFSs for prediction. The RUL estimation process is broken down into four generic processes: building a predictive model, selecting the most critical parameters, training the TNFS, and predicting RUL through the generated temporal fuzzy rules. Finally, the performance of the proposed TNFS is evaluated using a real preheater cement cyclone dataset. The results show that our TNFS produces better results than classical neuro-fuzzy systems and neural networks.