Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Manufacturing companies increasingly become "smarter" as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

The First Two Decades of Smart City Research from a Risk Perspective

Although they offer major advantages, smart cities present unprecedented risks and challenges. There are abundant discrete studies on risks related to smart cities; however, such risks have not been thoroughly understood to date. This paper is a systematic review that aims to identify the origin, trends, and categories of risks from previous studies on smart cities. This review includes 85 related articles published between 2000 and 2019. Through a thematic analysis, smart city risks were categorized into three main themes: organizational, social, and technological. The risks within the intersections of these themes were also grouped into (1) digital transformation, (2) socio-technical, and (3) corporate social responsibility. The results revealed that risk is a comparatively new topic in smart-city research and that little focus has been given to social risks. The findings indicated that studies from countries with a long history of smart cities tend to place greater emphasis on social risks. This study highlights the significance of smart city risks for researchers and practitioners, providing a solid direction for future smart-city research.

Assessment of the Design for Manufacturability Using Fuzzy Logic

The study proposes a procedure for assessing the designed manufacturing process for a new products. The purpose of the developed procedure is to evaluate the production process from the point of view of product design manufacturability of a unit and the small-lot production process. Evaluation of the design for the production process of a new product is based on criteria like process performance efficiency. Fuzzy logic-based methods were used to assess the designed process at different stages of its implementation—processing, assembly and organization of production. The developed method was illustrated by an example. The method presented in the study may be used by designers of production processes and employees of companies involved in the rationalization of already implemented production processes. The proposed method applies specifically to small-lot and unit production.

Models for Oil Refinery Waste Management Using Determined and Fuzzy Conditions

This study developed models to solve problems of optimisation, production, and consumption in waste management based on methods of system analysis. Mathematical models of the problems of optimisation and sustainable waste management in deterministic conditions and in a fuzzy environment were formulated. The income from production was maximised considering environmental standards that apply to the field of macroeconomics and microeconomics. The proposed approach used MANAGER software to formalise and solve the problem of revenue optimisation with production waste management to optimise the production of oil products with waste management at a specific technological facility of the Atyrau oil refinery in Kazakhstan. Based on the combined application of the principles of maximin and Pareto optimality, a formulation of the problem of production optimisation with waste management was obtained and a heuristic algorithm for solving the formulated fuzzy optimisation problem with waste management was developed.

Evaluation of Smart Alarm Systems for Industry 4.0 Technologies

Traditionally, the footwear industry is labor intensive, and cost control is key to ensuring shoe companies can be competitive. The development of Industry 4.0 concepts, used in high-tech industries and blockchain production information systems, enables the creation of smart factories with online alarm management systems, to improve manufacturing efficiency and reduce human resource requirements. In this paper, the performances of the causal association assessment model and the technique for order preference by similarity to the ideal solution (TOPSIS) model in evaluating large data blockchain technologies and quality online real-time early warning systems for production and raw material supplier management are compared, to increase the intelligence of production and to manage product traceability.

A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference

The standard manufacturing organizations follow certain rules. The highest ubiquitous organizing principles in infrastructure design are modular idea and symmetry, both of which are of the utmost importance. Symmetry is a substantial principle in the manufacturing industry. Symmetrical procedures act as the structural apparatus for manufacturing design. The rapid growth of population needs outstrip infrastructure such as roads, bridges, railway lines, commercial, residential buildings, etc. Numerous underground facilities are also installed to fulfill different requirements of the people. In these facilities one of the most important facility is water supply pipelines. Therefore, it is essential to regularly analyze the water supply pipelines’ risk index in order to escape from economic and human losses. In this paper, we proposed a simplified hierarchical fuzzy logic (SHFL) model to reduce the set of rules. To this end, we have considered four essential factors of water supply pipelines as input to the proposed SHFL model that are: leakage, depth, length and age. Different numbers of membership functions are defined for each factor according to its distribution. The proposed SHFL model takes only 95 rules as compared to the traditional mamdani fuzzy logic method that requires 1225 rules. It is very hard and time consuming for experts to design 1225 rules accurately and precisely. Further, we proposed a Do-it-Yourself (DIY) system for the proposed SHFL method. The purpose of the DIY system is that one can design the FIS model according to his or her need.

Sustainable Waste Management Drilling Process in Fuzzy Environment

Sustainable management issues of waste during drilling oil wells in marine conditions, the process of disposal of drill cuttings in the conditions of deficiency, and fuzzy initial information using fuzzy inference system are investigated. Based on the conducted system analysis, the main criteria for controlling the process of re-injection of suspended drill cuttings were analyzed and selected. We described the technology of preparation and injection of drill cuttings slurry into the underground horizon. The method of modeling and management of the process of disposal of drilling cuttings in the marine environment in a fuzzy environment with the use of fuzzy inference system, which helps to overcome the problems of scarcity and fuzziness of the original information due to the knowledge and experience of experts are proposed. The scheme and structure of the elements of the fuzzy inference system based on the Mamdani algorithm are given. The implementation of the fuzzy output system procedure was carried out in MatLab using Fuzzy Logic Toolbox. For the purpose of sustainable waste management in the process of oil production of marine fields, waste management tasks are formulated as a fuzzy mathematical programming problem, which takes into account economic and environmental criteria and many production constraints that may be fuzzy. Since the vector of such criteria is characterized by inconsistency, the developed methods for solving the set tasks of sustainable management are based on various tradeoff schemes modified to work in a fuzzy environment. The novelty and originality of the developed methods lies in the fact that, unlike the well-known methods of similar methods for solving fuzzy problems, they are set and solved without conversion to a system of equivalent deterministic problems, with-out losing the main part of the collected fuzzy information. This allows, through the full use of the original fuzzy information, to obtain a more adequate solution to the fuzzy problem of the real problem under production conditions.

Applied Research Towards Industry 4.0: Opportunities for SMEs

Industry 4.0 designates the recent digital revolution in the industrial sector, evolving from the comprehensive networking and automation of all the productive areas. Equipment, machinery, materials and products permit to (i) distinguish dealing out environmental settings and current status via sensors; (ii) join them through fixed software; and (iii) progress production procedures in an exclusive method. Additionally, Industry 4.0 exposes new trials to enterprises, especially small and medium-sized enterprises (SMEs). Firms should advance approaches to (i) achieve chances of innovation and digitalization; (ii) expand their processes; and (iii) define innovative business models. Based on these premises, a well-organized political, legal and infrastructural outline is essential to build up a business having an Industry 4.0 approach. Though bigger firms can get ahead through innovation processes and predicting the potential digitalization risks for their business models, SMEs may be in trouble. The present editorial aims to offer relevant research outcomes that has been carried out on such a current and emblematic theme, offering new perspectives and opportunities especially for SMEs.

Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System

As populations grow, facilities such as roads, bridges, railways lines, commercial and residential buildings, etc., must be expanded and maintained. There are extensive networks of underground facilities to fulfil the demand, such as water supply pipelines, sewage pipelines, metro structures, etc. Hence, a method to regularly assesses the risk of the underground facility failures is needed to decrease the chance of accidental loss of service or accidents that endanger people and facilities. In the proposed work, a cohesive hierarchical fuzzy inference system (CHFIS) was developed. A novel method is proposed for membership function (MF) determination called the heuristic based membership functions determination (HBMFD) method to determine an appropriate MF set for each fuzzy logic method in CHFIS. The proposed model was developed to decrease the number of rules for the full structure fuzzy inference system with all rule implementation. Four very crucial parameters were considered in the proposed work that are inputs to the proposed CHFIS model in order to calculate the risk of water supply pipelines. In order to fully implement the proposed CHFIS just 85 rules are needed while using the traditional Mamdani fuzzy inference system, 900 rules are required. The novel method greatly reduces implementation time and rule design sets that are extremely time consuming to develop and difficult to maintain.

An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.