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The ability to detect disturbances quickly as they arise in a supply chain helps to manage them efficiently and effectively. The purpose of this paper is to demonstrate the feasibility of automatically and therefore quickly detecting a specific disturbance, which is constrained capacity at a supply chain echelon.

Different supply chain echelons of a simulated four echelon supply chain were individually capacity constrained to assess their impacts on the profiles of system variables, and to develop a signature that related the profiles to the echelon location of the capacity constraint. A review of disturbance detection techniques across various domains formed the basis for considering the signature-based technique.

The signature for detecting a capacity constrained echelon was found to be based on cluster profiles of shipping and net inventory variables for that echelon as well as other echelons in a supply chain, where the variables are represented as spectra.

Detection of disturbances in a supply chain including that of constrained capacity at an echelon has seen limited research where this study makes a contribution.

The purpose of this paper is to theoretically analyze and experimentally demonstrate the investigation on and optimization of a distributed optical fiber sensor based on phase-sensitive optical time domain reflectometer (F-OTDR) for disturbance detection.

The F-OTDR system is investigated and optimized in two aspects: the hardware parameter and the interrogation scheme.

Based on the optimized hardware and the new interrogation scheme, the performances of the F-OTDR system have been improved greatly, compared with conventional F-OTDR system. A location accuracy of 2 m and a signal-to-noise ratio (SNR) of 16 dB have been achieved under a spatial resolution of 8 m. On the other hand, four disturbances at four different locations have been detected and located simultaneously, which is the most effective detection system with the maximum detection capability reported to date, to the best of the authors’ knowledge.

Four disturbances at four different locations have been detected and located simultaneously, which is the most effective detection system with the maximum detection capability reported to date, to the best of the authors’ knowledge. With same hardware conditions, more existing disturbances can be detected by using the new interrogation scheme, which is helpful to reduce the miss report of disturbance.

The purpose of this paper is to develop a new method for detection and classification of power quality disturbances such as transients, waveform distortions, sags, swells and interruptions.

For the purposes of the proposed method, the power quality disturbances are divided into two groups. Different algorithms are applied to detect and classify the disturbances from each of the two groups. For the processing of transients and waveform distortions, digital high‐pass filter and the mathematical morphology closing are used. Calculation of the RMS value is used for detection of sags, swells and interruptions.

The proposed method was implemented in a PC‐based measuring setup. The measuring setup was used in a seven‐months‐long monitoring of a single‐phase power system. In the course of the monitoring, the proposed method was verified on over 19,000 transients, 3,500 waveform distortions, 77 sags and 18 interruptions.

The classification stage of the proposed method does not differentiate between individual types of waveform distortions (harmonics, interharmonics, noise…).

The described approach is simpler and more reliable than, for example, methods based solely on wavelet transform. The proposed method is suitable for real‐time monitoring of power systems.

The paper describes a new and efficient way of detection and classification of disturbances (especially of transients and waveform distortions). It shows that mathematical morphology operations, which are normally used in image processing, represent a useful tool also in the field of power quality measurements.

Nowadays, an extra consumption of electric energy in the Colombian houses is generated due to electric or electronic elements plugged into the electric network. This fact produces a cost overrun in the user’s electricity bills. To reduce this extra cost, and also with a plus of reducing greenhouse gas emission, a monitoring system for the consumption of electric energy in a household will be designed and implemented to make electricity users realize how much money and energy is being wasted due to the unnecessary electric elements plugged into the network. This paper aims to show a monitoring system that allows the client to supervise the consumption of some appliances inside his/her home, remotely. It is also considered the HMI to be able to log in, choose the intervals of data and generate reports and graphics. The monitoring system is based on the integration of several technologies that are already used and implemented in houses and buildings, such as: measuring and treatment of data electronically using microcontrollers, Wi-Fi technology and dynamic graphic interface (website).

The methodology consists of several tasks, starting from documentation of the variables, instrumentation and methods for getting to the solution; the first part of the methodology focuses on selecting the electric and/or electronic elements to be monitored, so the instrumentation is able to monitor. Then, the power stage was implemented in this stage to measure signals from the sensors while sensing the electric nodes are adjusted, so does the transmission and reception. In the third stage, the design information system was implemented; this is where the received data from the sensors are stored and managed for further organization and visualization. Activities included the following: Analysis of the model of use cases: Identification of actors and actions that are involved in the system. Server selection: Study of the different server to manage the database. Design of the database: The variables, tables, fields, profiles are determined for managing the information. Connection between sensors and database: Correct data transmission and managing to the database from the sensors. Finally, the system is validated in a rural house for a month.

The monitoring system satisfies the main objective of making a tracing of the behavior of some appliances inside a house, showing graphically the instant current generated while connected, the cumulated energy consumed and the cost in Colombian pesos of the energy consumed so far, in real time.

The monitoring system requires the correct functioning of the sensors connected to each household appliance in the home.

The main approach in the monitoring platform is the real-time measurement of energy consumption by nodes (in each appliance) that allows the user to control the money. The innovative impact of the project will be based on the use of hardware and information systems in the measurement of electrical consumption.

This research has a direct impact on the economic aspects of the low-income population by allowing them to manage their energy consumption through the proposed system.

The main approach in the monitoring platform is the real-time measurement of energy consumption by nodes (in each appliance) that allows the user to control the money.

This research paper aims to investigate the change detection filter technique with a decision tree-based event (fault type) classifier for recognizing and categorizing power system disturbances on the high-voltage DC (HVDC) transmission link.

A change detection filter is used to the average and differential current components, which detects the point of fault initiation and records a change detection point (CDP). The half-cycle differential and average currents on both sides of the CDP are sent through the signal processing unit, which produces the respective target. The extracted target indices are sent through a decision tree-based fault classifier mechanism for fault classification.

In comparison with conventional differential current protection systems, the developed framework is faster in fault detection and classification and provides great accuracy. The new technology allows for prompt identification of the fault category, allowing electrical grids to be restored as quickly as possible to minimize economic losses. This novel technology enhances efficiency in terms of reducing computing complexity.

Setting a threshold value for identification is one of the limitations. To bring the designed system into stability condition before creating faults on it is another limitation. Reducing the computational burden is one of the limitations.

Creating a practical system in laboratory is difficult as it is a HVDC transmission line. Apart from that, installing rectifier and converter section for HVDC transmission line is difficult in a laboratory setting.

The suggested scheme’s importance and accuracy have been rigorously validated for the standard HVDC transmission system, subjected to various types of DC fault, and the results show the proposed algorithm would be a feasible alternative to real-time applications.

Islanding detection has become a serious concern due to the extensive integration of renewable energy sources. The non-detection zone (NDZ) and system-specific applicability, which are the two major issues with the islanding detection methods, are addressed here. The purpose of this paper is to devise an islanding detection method with zero NDZ and, which will be applicable to all types of renewable energy sources using the sequence components of the point of common coupling voltage.

Here, a parameter using the sequence components is derived to devise an islanding detection method. The parameter derived from the sequence components of point of common coupling voltage is analysed using wavelet transform. Various operating conditions, such as islanding and non-islanding, are considered for several test systems to evaluate the performance of the proposed method. All the simulations are carried out in Simulink/MATLAB environment.

The results showed that the proposed method has zero NDZ for both inverter- and synchronous generator-based renewable energy sources. In addition, the proposed method works satisfactorily as per the IEEE 1547 standards requirement.

Performance of the proposed method has been tested in several test systems and is found to be better than some conventional methods.

Resistance is expected to emerge with the implementation and use of new technologies in production systems. This work focuses on identifying sources of resistance to the use of Industry 4.0 technologies when managing production disturbances and suitable managerial approaches to deal with them.

A qualitative approach was chosen in this research. The authors conducted a literature review and a series of interviews. Thirty-one papers from the literature review were analysed, and 16 people from five different companies were interviewed.

The authors identified five different sources of resistance and three managerial approaches to dealing with them. The sources of resistance were based on (1) feelings of over-supervision, (2) unclear values, (3) feelings of inadequacy, (4) concerns about loss of power and jobs and (5) work overload. The three approaches to dealing with resistance are (1) communication, (2) participation and (3) training.

This work identifies the sources and strategies to deal with resistance to the use of Industry 4.0 technologies in the management of production disturbances. The managerial literature in this area is limited, and to the authors's knowledge, the specific sources for resistance and strategies to deal with that in this topic have not been systematically investigated before.

Monitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with improved yield. The history of process monitoring fault detection (PMFD) strategies can be traced back to 1930s. Thereafter various tools, techniques and approaches were developed along with their application in diversified fields. The purpose of this paper is to make a review to categorize, describe and compare the various PMFD strategies.

Taxonomy was developed to categorize PMFD strategies. The basis for the categorization was the type of techniques being employed for devising the PMFD strategies. Further, PMFD strategies were discussed in detail along with emphasis on the areas of applications. Comparative evaluations of the PMFD strategies based on some commonly identified issues were also carried out. A general framework common to all the PMFD has been presented. And lastly a discussion into future scope of research was carried out.

The techniques employed for PMFD are primarily of three types, namely data driven techniques such as statistical model based and artificial intelligent based techniques, priori knowledge based techniques, and hybrid models, with a huge dominance of the first type. The factors that should be considered in developing a PMFD strategy are ease in development, diagnostic ability, fault detection speed, robustness to noise, generalization capability, and handling of nonlinearity. The review reveals that there is no single strategy that can address all aspects related to process monitoring and fault detection efficiently and there is a need to mesh the different techniques from various PMFD strategies to devise a more efficient PMFD strategy.

The review documents the existing strategies for PMFD with an emphasis on finding out the nature of the strategies, data requirements, model building steps, applicability and scope for amalgamation. The review helps future researchers and practitioners to choose appropriate techniques for PMFD studies for a given situation. Further, future researchers will get a comprehensive but precise report on PMFD strategies available in the literature to date.

The review starts with identifying key indicators of PMFD for review and taxonomy was proposed. An analysis was conducted to identify the pattern of published articles on PMFD followed by evolution of PMFD strategies. Finally, a general framework is given for PMFD strategies for future researchers and practitioners.

This paper aims to explore the concept of resilience set within a family business context and considers how familiness and the nature of noneconomic factors, such as relationship dynamics influence performance. This paper provides new insights into the nature and impact of familiness as a mediating device, uncovering the potential for reframing resilience theory and practice.

The paper draws on a review of the extant literature in the areas of resilience and familiness as a means of developing a deeper understanding of the social-ecological system of the family firm.

The study reveals family business as a complex interrelationship between complimentary social-ecological systems. It highlights the complexity of family business and the challenges of the relational nature of familiness and how this presents additional layers of complexity in the decision making process and implementation.

The paper draws on literature that is dominated by western culture and may partially or not at all reflect the issues associated with organisational resilience in family firms with such backgrounds and their culturally bound social-ecological systems.

The paper seeks to fill a knowledge gap by exploring the key elements of organisational resilience in the context of familiness. The work calls for further research into the nature of familiness connections mediating the nature of family relational dynamics. It further provides a framework indicating how these elements can shape and subvert day-to-day management events, raising implications for theory and practice and calls for deeper empirical research to be undertaken.