An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions

Abstract

The architecture design of industrial data analytics system addresses industrial process challenges and the design phase of the industrial Big Data management drivers that consider the novel paradigm in integrating Big Data technologies into industrial cyber-physical systems (iCPS). The goal of this paper is to support the design of analytics Big Data solutions for iCPS for the modeling of data elements, predictive analysis, inference of the key performance indicators, and real-time analytics, through the proposal of an architecture that will support the integration from IIoT environment, communications, and the cloud in the iCPS. An attribute driven design (ADD) approach has been adopted for architectural design gathering requirements from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair, and overhaul (MRO) scenarios. Data management drivers presented consider new Big Data modeling analytics techniques that show data is an invaluable asset in iCPS. An architectural design reference for a Big Data analytics architecture is proposed. The before-mentioned architecture supports the Industrial Internet of Things (IIoT) environment, communications, and the cloud in the iCPS context. A fault diagnosis case study illustrates how the reference architecture is applied to meet the functional and quality requirements for Big Data analytics in iCPS.

Application of cloud computing platform in industrial big data processing

“…Through the cloud platform, enterprises can effectively solve the problems of data storage and management, and at the same time use the powerful data processing and analysis capabilities of the cloud to mine and analyze big data (See Figure 2). This not only greatly improves the efficiency and speed of data processing, but also helps enterprises extract valuable information from huge data sets, thereby optimizing the decisionmaking process, improving operational efficiency and innovation capabilities [2].…”

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

“…One of the keys to smart manufacturing is intelligent fault diagnosis based on big real-time data flow analysis to support timely and accurate production decisions [29]. With the development of networked technologies such as the Industrial Internet of Things (IIoT) [30,31] and cyberphysical systems (CPS) [32][33][34], it has become easier to obtain large streams of operational status data in real time [35,36]. For example, large-scale data processing technologies such as cloud computing and digital twins [37] enable easy real-time condition monitoring, online fault detection, diagnosis, and prediction of industrial processes and mechanical equipment [38].…”

Decision-Making under the Risk, Uncertainty and COVID-19 Pandemic Conditions Applying the PL9A Method of Logistics Planning—Case Study

“…An innovative approach to the planning process could take into account modeling of correlations between the accumulation of capital, resources, and changes in demand and supply, ecological factors, and energy efficiency. Achieving excellence in logistics planning processes cannot be done without the use of Big Data analytics [2,3] and reduction of the tendency of organizations to make high-risk decisions.…”

“…In order to illustrate the logistic processes taking place in companies with a logistic business orientation, the logistic processes are presented in a phase approach in Figure 2. Considering the quality of the planning process and its relationship to production [60,61], it seems reasonable to isolate organizational units specializing in an integrated and process approach to processing information [3] and goods flow between the supply, production, and distribution phases. This can provide a needed innovation such as a restricted unit dealing with a well-defined set of tasks.…”