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Industrial Internet of Things: Basics
Published in Sandeep Misra, Chandana Roy, Anandarup Mukherjee, Introduction to Industrial Internet of Things and Industry 4.0, 2021
Sandeep Misra, Chandana Roy, Anandarup Mukherjee
In the heavy industries, industrial processes form the basic core component of operations. These industrial processes are the methods which involve various physical, chemical, mechanical, or electrical stages to manufacture a product, which usually occurs on a large-scale. With the inclusion of advanced technologies in the industrial processes, IIoT forecasts the improvement of quality, safety, and productivity. As per a survey by PWC [72], the rate of process advancements is predicted to improve more than 70% by the year 2020 in various industrial sectors such as electronics, manufacturing, construction, chemical, defense, and transportation. With the completion of the transformation process, a typical industrial enterprise will be transformed into digital enterprises. Physical assets form the core of the industries, whereas the digital interface and real-time data-based services surround them. The digital transformation in the processes primarily aims to transform individual companies and the market dynamics across a wide range of industries.
There’s Three Kinds of Industries
Published in Peter Middleton, James Sutton, Lean Software Strategies, 2020
Lean production shares some characteristics of each of the previous stages but compensates for their weaknesses and adds new strengths of its own. It greatly broadens the focus to include all stakeholders; owners and investors to be sure, but also customers (often even more strongly than owners), employees, and public concerns. More importantly, unlike craft and mass production, lean production removes waste from—and continuously improves industrial processes by—effectively utilizing employees, equipment, and capital to produce and deliver products that satisfy each customer.
Performance evaluation of moving average-based EWMA chart for exponentially distributed process
Published in Journal of the Chinese Institute of Engineers, 2020
Saddam Akber Abbasi, Muhammad Abid, Muhammad Riaz, Hafiz Zafar Nazir
Shewhart (1931) introduced the concept of statistical process control (SPC), and in particular control charts, as means of monitoring industrial processes and controlling the quality of manufactured products. In SPC, a distinction is often made between two types of variability; one due to chance causes and the other that results from special or assignable causes. Montgomery (2005) defines chance causes as an inherent part of the process. Moreover, Duncan (1965) mentioned that chance variations behave in a random manner, and they do not show any defined pattern. However, they follow certain statistical laws. If only chance causes affect the process, the process is said to be under control. On the other hand, the variation occurring due to assignable causes is much larger than the variation due to chance causes. The existence of assignable causes leads to a process being considered out-of-control.
A comparative study on Poisson control charts
Published in Quality Technology & Quantitative Management, 2020
Vasileios Alevizakos, Christos Koukouvinos
Statistical process control (SPC) is a useful tool of the statistical quality control (SQC) which employs statistical methods to monitor and control the quality of industrial processes. Control charts are a very powerful tool of the SPC and are used to monitor the parameters of a process. Shewhart control charts are the most popular control charts, as they are very user-friendly. There are two main types of control charts: (i) the control charts for measurement data and (ii) the control charts for attribute data, such as the number of nonconformities or the number of defective products in a production unit. The most well-known Shewhart control charts for attribute data are the -chart and the -chart for binomial-distributed data and the -chart and the -chart for Poisson distributed data (Montgomery, 2013). Unfortunately, the Shewhart -chart is very insensitive to detect small process shifts (say, ) and under certain circumstances, it cannot detect a downward process shift.
Robust Adaptive Control of a Quadruple Tank Process with Sliding Mode and Pole Placement Control Strategies
Published in IETE Journal of Research, 2023
Alhassan Osman, Tolgay Kara, Mehmet Arıcı
Industrial processes are essentially physical systems that combine a sequence of operations in interaction with one another, with the purpose of fulfilling a certain industrial requirement. These interactions in the majority of cases involve multiple variables, which increases the complexity of the process, thereby making it more difficult to control compared to Single Input Single Output (SISO) systems [1–4]. Process control problems, involving multivariable processes, have been extensively discussed, analyzed, and simulated over the past decades using methods and tools of automatic control in order to improve the performances of the system [5–8].