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This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.

The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.

The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.

The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.

Contemporary supply chain relationships inherently rely on delegation of work between organizations and, thus, are subject to agency problems for which a wide range of governance mechanisms exist. This review of agency theory (AT), across four distinct fields, explains the connection between governance mechanisms and supply chain relationship types.

The study uses a systematic literature review (SLR) of articles using AT in a supply chain context from the operations and supply chain management, general management, marketing, and economics fields.

The authors categorize the governance mechanisms identified to create a typology of agency relationships in supply chains.

The developed typology provides parsimonious theory on different forms of supply chain agency relationships and takes a step towards a “supply chain-oriented agency theory” explaining and predicting relationship types and governance in supply chains. Furthermore, a future research agenda calls for more accurate measuring of agency costs, to examine residual gains alongside residual losses, to take a dual-sided perspective of agency relations and to adopt AT to examine more complex supply networks.

The review provides a menu of governance mechanisms and describes situations under which these mechanisms could be deployed to guide managers when developing their supply chain relationships.

The first review to combine and elaborate views from four major disciplines using AT as a lens to supply chain relationships. Expanding the traditional set of governance mechanisms provides academics and practitioners with a bigger “menu” of options to consider.

For many decades, it has been recognized that maintenance activities should be adapted to the specific usage of a system. For that reason, many advanced policies have been developed, such as condition-based and load-based maintenance policies. However, these policies require advanced monitoring techniques and rather detailed understanding of the failure behavior, which requires the support of an OEM or expert, prohibiting application by an operator in many cases. The present work proposes a maintenance policy that relieves the high (technical) demands set by these existing policies and provides a more accurate specification of the required (dynamic) maintenance interval than traditional usage-based maintenance.

The methodology followed starts with a review and critical assessment of existing maintenance policies, which are classified according to six different aspects. Based on the need for a technically less demanding policy that appears from this comparison, a new policy is developed. The consecutive steps required for this functional usage profiles based maintenance policy are then critically discussed: usage profile definition, monitoring, profile severity quantification and the possible extension to the fleet level. After the description of the proposed policy, it is demonstrated in three case studies on real systems.

A maintenance policy based on a simple usage registration procedure appears to be feasible, which enables a significantly more efficient maintenance process than the traditional usage-based policies. This is demonstrated by the policy proposed here.

The proposed maintenance policy based on functional usage profiles offers the operators of fleets of systems the opportunity to increase the efficiency and effectiveness of their maintenance process, without the need for a high investment in advanced monitoring systems and in experts interpreting the results.

The original contribution of this work is the explicit definition of a new maintenance policy, which combines the benefits of considering the effects of usage or environment severity with a limited investment in monitoring technology.

As evident from the literature review, the research on cyber security performance is centered on security metrics, maturity models, etc. Essentially, all these are helpful for evaluating the efficiency of cyber security organization but what matters is how the factors of internal efficiency affect the business performance, i.e. the external effectiveness. The purpose of this research paper is to derive the factors of internal efficiency and external effectiveness of cyber security and develop impact model to identify the most and least preferred parameters of internal efficiency with respect to all the parameters of external effectiveness.

There are two objectives for this research: Deriving the factors of internal efficiency and external effectiveness of cyber security; Developing a model to identify the impact of internal efficiency factors on the external effectiveness of cyber security since there is not much evidence of research in defining the factors of internal efficiency and external effectiveness of cyber security, the authors have chosen grounded theory methodology (GTM) to derive the parameters. In this study emic approach of GTM is followed and an algorithm is developed for administering the grounded theory research process. For the second research objective survey methodology and rank order was used to formulate the impact model. Two different samples and questionnaires were designed for each of the objectives.

For the objective 1, 11 factors of efficiency and 10 factors of effectiveness were derived. These are used as independent and dependent variable respectively in the later part of the research for the second objective. For the objective 2 the impact models among independent and dependent variables were formulated to find out the following. Most and least preferred parameters lead to internal efficiency of cyber security organization to identify the most and least preferred parameters of internal efficiency with respect to all the parameters external effectiveness.

The factors of internal efficiency and external effectiveness constructed by using grounded theory cannot remain constant in the long run, because of dynamism of the domain itself. Over and above this, there are inherent limitations of the tools like grounded theory, used in the research. Few important limitations of GTM are as below in grounded theory, it is comparatively difficult to maintain and demonstrate the rigors of research discipline. The sheer volume of data makes the analysis and interpretation complex, and lengthy time consuming. The researchers’ presence during data gathering, which is often unavoidable and desirable too in qualitative research, may affect the subjects’ responses. The subjectivity of the data leads to difficulties in establishing reliability and validity of approaches and information. It is difficult to detect or to prevent researcher-induced bias.

The internal efficiency and external effectiveness factors of cyber security can be further correlated by the future researchers to understand the correlations among all the factors and predict cyber security performance. The grounded theory algorithm developed by us can be further used for qualitative research for deriving theory through abstractions in the areas where there is no sufficient availability of data. Practitioners of cyber security can use this research to focus on relevant areas depending on their respective business objective/requirements. The models developed by us can be used by the future researchers to for various sectoral validations and correlations.

Though the financial costs of a cyber-attack are steep, the social impact of cyber security failures is less readily apparent but can cause lasting damage to customers, employees and the company. Therefore, it is always important to be mindful of how the impact of cyber security affects society as well as the bottom line when they are calculating the potential impact of a breach. Underestimating either impact can destroy a brand. The factor of internal efficiency and external effectiveness derived by us will help stakeholder in focusing on relevant area depending on their business. The impact model developed in this research is very useful for focusing a particular business requirement and accordingly tune the efficiency factor.

During literature study the authors did not find any evidence of application of grounded theory approach in cyber security research. While the authors were exploring research literature to find out some insight into the factor of internal efficiency and external effectiveness of cyber security, the authors did not find concrete and objective research on this. This motivated us to use grounded theory to derive these factors. This, in the authors’ opinion is one of the pioneering and unique contribution to the research as to the authors’ knowledge no researchers have ever tried to use this methodology for the stated purpose and cyber security domain in general. In this process the authors have also developed an algorithm for administering GTM. Further developing impact models using factors of internal efficiency and external effectiveness has lots of managerial and practical implication.

The microseismic monitoring technique has great advantages on identifying the location, extent and the mechanism of damage process occurring in rock mass. This study aims to analyze distribution characteristics and the evolution law of excavation damage zone of surrounding rock based on microseismic monitoring data.

In situ test using microseismic monitoring technique is carried out in the large-span transition tunnel of Badaling Great Wall Station of Beijing-Zhangjiakou high-speed railway. An intelligent microseismic monitoring system is built with symmetry monitoring point layout both on the mountain surface and inside the tunnel to achieve three-dimensional and all-round monitoring results.

Microseismic events can be divided into high density area, medium density area and low density area according to the density distribution of microseismic events. The positions where the cumulative distribution frequencies of microseismic events are 60 and 80% are identified as the boundaries between high and medium density areas and between medium and low density areas, respectively. The high density area of microseismic events is regarded as the high excavation damage zone of surrounding rock, which is affected by the grade of surrounding rock and the span of tunnel. The prediction formulas for the depth of high excavation damage zone of surrounding rock at different tunnel positions are given considering these two parameters. The scale of the average moment magnitude parameters of microseismic events is adopted to describe the damage degree of surrounding rock. The strong positive correlation and multistage characteristics between the depth of excavation damage zone and deformation of surrounding rock are revealed. Based on the depth of high excavation damage zone of surrounding rock, the prestressed anchor cable (rod) is designed, and the safety of anchor cable (rod) design parameters is verified by the deformation results of surrounding rock.

The research provides a new method to predict the surrounding rock damage zone of large-span tunnel and also provides a reference basis for design parameters of prestressed anchor cable (rod).

The purpose of the study is to consolidate a set of indicators for assessing design and construction phase strategies for reducing operational greenhouse gas (GHG) emission. They will also estimate the quantity of operational GHG emission and its associated reduction over assessment period.

Five steps framework adopted include defining the purpose of the indicators and selection of candidate indicators. Others are defining the criteria for indicator selection, selecting and defining the proposed indicators. Relevancy, measurability, prevalence, preference, feasibility and adaptability of the indicator were the criteria used for selecting the indicators.

The study consolidated public transport accessibility, sustainable parking space, green vehicle priority, proximity to amenities and alternative modes as indicators for design and construction phase strategies. Transportation accounting and carbon footprint (CFP) and their associated reduction are indicators for operational GHG emission while plan and policy is an indicator for policymakers and stakeholders.

The study shows that providing correct indicators for assessing direct and indirect GHG emission with easy to obtain data is essential for assessment of built environment. Stakeholder can use the indicators in developing new rating systems and researchers as an additional knowledge. Policy makers and stakeholders can use the study in monitoring and rewarding the sustainability and activities of building related industries and organisations.

The study was conducted at the Center for Energy and Industrial Environmental Studies (CEIES) Universiti Tun Hussein Onn Malaysia and utilises existing rating systems and tools, Intergovernmental Panel on Climate Change (IPCC) and GHG protocol reports and guides and several other standards, which are open for research.

This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

This study aims to focus on verifying the possibility of monitoring the condition of a turboprop engine using data recorded by on-board avionics Garmin G1000. This approach has potential benefits for operators without the need to invest in specialised equipment. The main focus was on the inter-turbine temperature (ITT). An unexpected increase in temperature above the usual value may indicate an issue with the engine. The problem lies in the detection of small deviations when the absolute value of the ITT is affected by several external variables.

The ITT is monitored by engine sensors and stored by avionics 1× per second onto an SD card. This process generates large amount of data that needs to be processed. Therefore, an algorithm was created to detect the steady states of the engine parameters. The ITT value also depends on the flight parameters and surrounding environment. As a solution to these effects, the division of data into clusters that represent the usual flight profiles was tested. This ensures a comparison at comparable ambient pressures. The dominant environmental influence then remain at the ambient air temperature (OAT). Three OAT compensation methods were tested in this study. Compensation for the standard atmosphere, compensation for the standard temperature of the given flight level and compensation for the speed of the generator, where the regression analysis proved the dependence between the ambient temperature and the speed of the generator.

The influence of ambient temperature on the corrected ITT values is noticeable. The best method for correcting the OAT appears to be the use of compensation through the revolutions of the compressor turbine NG. The speed of the generator depends on several parameters, and can refine the corrected ITT value. During the long-term follow-up, the ITT differences (delta values) were within the expected range. The tested data did not include the behaviour of the engine with a malfunction or other damage that would clearly verify this approach. Therefore, the engine monitoring will continue.

This study presents a possible approach to turbine engine condition monitoring using limited on board avionic data. These findings can support the development of an engine condition monitoring system with automatic abnormality detection and low operating costs.

This article represent a practical description of problems in monitoring the condition of a turboprop engine in an aircraft with variable flight profiles. The authors are not aware of a similar method that uses monitoring of engine parameters at defined flight levels. Described findings should limit the influence of ambient air pressure on engine parameters.