Open-loop unstable systems with time-delays are often encountered in process industry, which are often more difficult to control than stable processes. In this paper, the stabilization by PID controller of second-order unstable processes, which can be represented as second-order deadtime with an unstable pole (SODUP) and second-order deadtime with two unstable poles (SODTUP), is performed via the necessary and sufficient criteria of Routh-Hurwitz stability analysis. The stability analysis provides improved understanding on the existence of a stabilizing range of each PID parameter. Three simple PID tuning algorithms are proposed to provide desired closed-loop performance-robustness within the stable regions of controller parameters obtained via the stability analysis. The proposed PID controllers show improved performance over those derived via some existing methods.
This study explores the determinants of the export performance of Indonesia's low-, medium-, and high-technology manufacturing industries by focusing on the role of raw-material imports and technical efficiency. Micro firm-level data from 2010-2015 were utilized for the analysis in this study. The stochastic frontier analysis was employed to measure technical inefficiency and to determine its effect on export performance. Our findings indicate that in all categories of industry technical efficiency, raw materials imports, foreign direct investment (FDI), location, firm size, labour productivity, and concentration of industries were significant determinants of export performance. While high efficiency increases exports in low- and medium-technology firms, exports decrease in firms with high efficiency accompanied by high imports, FDI, size, and labour productivity. Furthermore, in high-technology industries, efficiency reduces exports and again increases them when mediated by a concentration of industries and location. The empirical strategy also supports the positive effect of imports on export performance in both industries, which also aligns with decreased exports in firms with high imports accompanied by high FDI, efficiency, labour productivity, the concentration of industries, and size. To this end, the study has implications for low-, medium-, and high-technology manufacturing that are mainly concerned with increasing exports.
The cumulative sum (CUSUM) control chart is widely used in industry for the detection of small and moderate shifts in process location and dispersion. For efficient monitoring of process variability, we present several CUSUM control charts for monitoring changes in standard deviation of a normal process. The newly developed control charts based on well-structured sampling techniques - extreme ranked set sampling, extreme double ranked set sampling and double extreme ranked set sampling, have significantly enhanced CUSUM chart ability to detect a wide range of shifts in process variability. The relative performances of the proposed CUSUM scale charts are evaluated in terms of the average run length (ARL) and standard deviation of run length, for point shift in variability. Moreover, for overall performance, we implore the use of the average ratio ARL and average extra quadratic loss. A comparison of the proposed CUSUM control charts with the classical CUSUM R chart, the classical CUSUM S chart, the fast initial response (FIR) CUSUM R chart, the FIR CUSUM S chart, the ranked set sampling (RSS) based CUSUM R chart and the RSS based CUSUM S chart, among others, are presented. An illustrative example using real dataset is given to demonstrate the practicability of the application of the proposed schemes.
A monitoring procedure was introduced for process variability in a multivariate setting based on individual observations which was a combination of (i) robust high breakdown point approach in the set-up stage to determine the reference sample and (ii) the use of Wilks chart in the mass production stage. This setting is what the Malaysian manufacturing industry is currently lacking in, especially when a robust approach must be used. The advantage of this procedure was revealed by using the case of a female shrouded connector production process in a Malaysian industry. Moreover, this procedure could also be used in any process quality monitoring and for any industry. A recommendation for quality practitioners was also addressed.
Matched MeSH terms: Industry; Manufacturing Industry
Successful implementation of the lean concept as a sustainable approach in the construction industry requires the identification of critical drivers in lean construction. Despite this significance, the number of in-depth studies toward understanding the considerable drivers of lean construction implementation is quite limited. There is also a shortage of methodologies for identifying key drivers. To address these challenges, this paper presents a list of all essential drivers within three aspects of sustainability (social, economic, and environmental) and proposes a novel methodology to rank the drivers and identify the key drivers for successful and sustainable lean construction implementation. In this regard, the entropy weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed in this research. Subsequently, an empirical study was conducted within the Malaysian construction industry to demonstrate the proposed method. Moreover, sensitivity analysis and comparison with the existing method were engaged to validate the stability and accuracy of the achieved results. The significant results obtained in this study are as follows: presenting, verifying and ranking of 63 important drivers; identifying 22 key drivers; proposing an MCDM model of key drivers. The outcomes show that the proposed method in this study is an effective and accurate tool that could help managers make better decisions.
Background: A major player in industry is the induction motor. The constant motion and mechanical nature of motors causes much wear and tear, creating a need for frequent maintenance such as changing contact brushes. Unmannered and infrequent monitoring of motors, as is common in the industry, can lead to overexertion and cause major faults. If a motor fault is detected earlier through the use of automated fault monitoring, it could prevent minor faults from developing into major faults, reducing the cost and down-time of production due the motor repairs. There are few available methods to detect three-phase motor faults. One method is to analyze average vibration signals values of V, I, pf, P, Q, S, THD and frequency. Others are to analyze instantaneous signal signatures of V and I frequencies, or V and I trajectory plotting a Lissajous curve. These methods need at least three sensors for current and three for voltage for a three-phase motor detection. Methods: Our proposed method of monitoring faults in three-phase industrial motors uses Hilbert Transform (HT) instantaneous current signature curve only, reducing the number of sensors required. Our system detects fault signatures accurately at any voltage or current levels, whether it is delta or star connected motors. This is due to our system design, which incorporates normalized curves of HT in the fault analysis database. We have conducted this experiment in our campus laboratory for two different three-phase motors with four different fault experiments. Results: The results shown in this paper are a comparison of two methods, the V and I Lissajous trajectory curve and our HT instantaneous current signature curve. Conclusion: We have chosen them as our benchmark as their fault results closely resemble our system results, but our system benefits such as universality and a cost reduction in sensors of 50%.
LoRa is an ISM-band based LPWAN communication protocol. Despite their wide network penetration of approximately 20 kilometers or higher using lower than 14 decibels transmitting power, it has been extensively documented and used in academia and industry. Although LoRa connectivity defines a public platform and enables users to create independent low-power wireless connections while relying on external architecture, it has gained considerable interest from scholars and the market. The two fundamental components of this platform are LoRaWAN and LoRa PHY. The consumer LoRaWAN component of the technology describes the network model, connectivity procedures, ability to operate the frequency range, and the types of interlinked gadgets. In contrast, the LoRa PHY component is patentable and provides information on the modulation strategy which is being utilized and its attributes. There are now several LoRa platforms available. To create usable LoRa systems, there are presently several technical difficulties to be overcome, such as connection management, allocation of resources, consistent communications, and security. This study presents a thorough overview of LoRa networking, covering the technological difficulties in setting up LoRa infrastructures and current solutions. Several outstanding challenges of LoRa communication are presented depending on our thorough research of the available solutions. The research report aims to stimulate additional research toward enhancing the LoRa Network capacity and allowing more realistic installations.
Due to significant requirement of energy, water, material, and other resources, the manufacturing industries significantly impact environmental, economic, and social dimensions of sustainability (triple bottom-line). In response, today's research is focused on finding solution towards sustainable manufacturing. In this regard, sustainability assessment is an essential strategy. In the past, a variety of tools was developed to evaluate the environmental dimension. Because of this fact, previous review studies were grounded mostly on tools for green manufacturing. Unlike previous review articles, this study was aimed to review and analyze the emerging sustainability assessment methodologies (published from 2010 to 2020) for manufacturing while considering the triple bottom-line concept of sustainability. In this way, the paper presents a decade review on this topic, starting from 2010 as the guidelines for the social dimension became available in 2009. This paper has analyzed various methods and explored recent progress patterns. First, this study critically reviewed the methods and then analyzed their different integrating tools, sustainability dimensions, nature of indicators, difficulty levels, assessment boundaries, etc. The review showed that life cycle assessment and analytic hierarchy process-based approaches were most commonly used as integrating tools. Comparatively, still, environmental dimension was more commonly considered than economic and social dimensions by most of the reviewed methods. From indicators' viewpoint, most of the studied tools were based on limited number of indicators, having no relative weights and validation from the experts. To overcome these challenges, future research directions were outlined to make these methods more inclusive and reliable. Along with putting more focus on economic and social dimensions, there is a need to employ weighted, validated, and applicable indicators in sustainability assessment methods for manufacturing.
This study aims to give a comprehensive analysis of customers' acceptance and use of AI gadgets and its relevant ethical issues in the tourism and hospitality business in the era of the Internet of Things. Adopting a PRISMA methodology for Systematic Reviews and Meta-Analyses, the present research reviews how tourism and hospitality scholars have conducted research on AI technology in the field of tourism and the hospitality industry. Most of the journal articles related to AI issues published in Web of Science, ScienceDirect.com and the journal websites were considered in this review. The results of this research offer a better understanding of AI implementation with roboethics to investigate AI-related issues in the tourism and hospitality industry. In addition, it provides decision-makers in the hotel industry with practical references on service innovation, participation in the design of AI devices and AI device applications, meeting customer needs, and optimising customer experience. The theoretical implications and practical interpretations are further identified.
The expanding global Muslim population has increased the demand for halal pharmaceuticals. However, there are several challenges for this emerging niche industry, foremost of which is the need to establish a proper, well-regulated, and harmonized accreditation and halal management system.
Matched MeSH terms: Drug Industry/standards; Drug Industry/ethics*
Fused deposition modelling (FDM) opens new ways across the industries and helps to produce complex products, yielding a prototype or finished product. However, it should be noted that the final products need high surface quality due to their better mechanical properties. The main purpose of this research was to determine the influence of computer numerical control (CNC) machining on the surface quality and identify the average surface roughness (Ra) and average peak to valley height (Rz) when the specimens were printed and machined in various build orientations. In this study, the study samples were printed and machined to investigate the effects of machining on FDM products and generate a surface comparison between the two processes. In particular, the block and complex specimens were printed in different build orientations, whereby other parameters were kept constant to understand the effects of orientation on surface smoothness. As a result, wide-ranging values of Ra and Rz were found in both processes for each profile due to their different features. The Ra values for the block samples, printed samples, and machined samples were 21, 91, and 52, respectively, whereas the Rz values were identical to Ra values in all samples. These results indicated that the horizontal surface roughness yielded the best quality compared to the perpendicular and vertical specimens. Moreover, machining was found to show a great influence on thermoplastics in which the surfaces became smooth in the machined samples. In brief, this research showed that build orientation had a great effect on the surface texture for both processes.
An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things-intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic.
Growing environmental deterioration has caused many countries to tighten their environmental regulations across the globe. Recent studies show that most developed countries enforced stricter environmental regulations creating a pollution haven to developing countries such as Nigeria. Studies show the non-availability of an environmental regulation compliance scale in the energy sectors. The aim of this paper is to validate the effects of environmental regulation compliance scale for oil and gas companies' operations in the Nigerian oil and gas industry. Hence, an adapted questionnaire comprising 11 items was administered to 300 local and multinational oil and gas companies in Nigeria. All the items were subjected to evaluations and validations by eight expert reviewers with cognate experience in oil and gas activities. Evaluation of the reliability and validity of the measures of the environmental regulation scale was performed through confirmatory factor analysis (CFA) using SPSS version 25 and PLS-SEM version 3.8. The results provide evidence that the environmental regulation compliance scale has met the reliability and validity criteria. Consequently, policymakers, practitioners, and researchers can adapt this scale to assess the effects of environmental regulation compliance by companies in different jurisdictions across the globe. This study undoubtedly builds the existing literature and contributes to the subject area; by implication, the validated scale will assist host oil and gas countries with stringent environmental regulations to come up with policies in such a way as to ensure not chasing away the current investors or discouraging prospective ones from investing in their countries.
Matched MeSH terms: Oil and Gas Industry*; Industry*
This paper proposed seven existing and new performance indicators to measure the effectiveness of quality management system (QMS) maintenance and practices in construction industry. This research is carried out with a questionnaire based on QMS variables which are extracted from literature review and project performance indicators which are established from project management's theory. Data collected was analyzed using correlation and regression analysis. The findings indicate that client satisfaction and time variance have positive and significant relationship with QMS while other project performance indicators do not show significant results. Further studies can use the same project performance indicators to study the effectiveness of QMS in different sampling area to improve the generalizability of the findings.