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Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks during the product design and manufacturing phases. Risk priority number (RPN), a risk assessment indicator used in FMEA, is widely used in the field due to its simple calculation process, but its limitations as an absolute risk assessment indicator have been pointed out. There has also been criticism of the unstructured nature and lack of systematicity in the FMEA procedures. This work proposes an expected loss-FMEA (EL-FMEA) model that organizes FMEA procedures and structures quantitative risk assessment metrics. In the EL-FMEA model, collectible maintenance record data is defined and based on this, the failure rate of components and systems and downtime and uptime of the system are calculated. Moreover, based on these calculated values, the expected economic loss is computed considering the failure detection time. It also provides an alternative coefficient to evaluate whether or not a detection system is installed to improve the expected loss of failure. Finally, a case study was conducted based on the maintenance record data, and the application procedure of the EL-FMEA model was presented in detail, and the practicality of this model was verified through the results.

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

This paper proposes an idea for modeling of matching between agents based on utility theory. The basic idea comes from the stable matching problem, and this focuses on how an agent makes an ultimate decision when it comes to finding a matching agent based on utilities that other agents have as well as the agent's preferences for each utility option. This paper begins with introductions of agent organization, partner selection in Agent- Based Model, and utility theories. The proposed approach of this paper includes aspects of directions and applications. In terms of the aspect of directions, pre-defined concepts such as utility options, perceptual and actual utility, and deal-breakers are introduced to understand applications better. Also, two possible scenarios with basic and high complexity were handled as applications for partner matching between agents based on utilities. There are three merits of the proposed approach, which are: (1) both the agent's benefit and its potential partner agent's benefits are considered for mate selection; (2) by employing Congregation Organizational Paradigm, an agent's partner selection approach can be applied in dynamic environments by seeking its optimal partner in another networking cluster; and (3) by employing threshold and deal-breakers, a rational agent can avoid making a decision which lowers its level of happiness or benefits.

Experimental results pertaining to natural pharmaceutical complex products (NPCPs) often exhibit large variabilities in their associated response variables. To improve the quality of an NPCP, systemic studies (i.e., statistical analysis and mathematical optimization), including variability analysis and robust optimization, are often required. To this end, a systemic approach for an NPCP development process is proposed by integrating robust design and optimization methodologies. A quality function deployment method can be used to systematically define a standardized manufacturing process and relevant process variables for Chong Kun Dang (CKD)-497. Based on those variables, an experiment is designed using response surface methodology to mathematically estimate the output response functions associated with input variables. In addition, a design space (DS), which can guarantee the quality of an NPCP, is demonstrated by utilizing the overlaid contour plots of the estimated response functions. Finally, a CKD-497 case study is conducted for verification and validation.

The accelerated urbanization of countries has led the adoption of the smart power grid with an explosion in high power usage. The emergence of Non-intrusive load monitoring (NILM), also referred to as Energy Disaggregation has followed the recent worldwide adoption of smart meters in smart grids. NILM is a convenient process to analyze composite electrical energy load and determine electrical energy consumption.A number of state-of-the-art NILM (energy disaggregation) algorithms have been proposed recently to detect various individual appliances from one aggregated signal observation. Different kinds of classification methods such as Hidden Markov Model (HMM), Support Vector Method (SVM), neural networks, fuzzy logic, Naive Bayes, k-Nearest Neighbors (kNN), and many other hybrid approaches have been used to classify the estimated power consumption of electrical appliances from extracted appliances signatures. This study proposes an end-to-end edge computing system with an NILM algorithm, which especially focuses on recognizing Electric Vehicle (EV) charging. This system consists of three main components: (1) Data acquisition and Preprocessing, (2) Extraction of EV charging load via an NILM algorithm (Load identification) on the NILMTK Framework, (3) and Result report to the cloud server platform.The monitoring of energy consumption through the proposed system is remarkably beneficial for demand response and energy efficiency. It helps to improve the understanding and prediction of power grid stress as well as enhance grid system reliability and resilience of the power grid. Furthermore, it is highly advantageous for the integration of more renewable energies that are under rapid development. As a result, countless potential NILM use-cases are expected from monitoring and identifying energy consumption in a power grid. It would enable smarter power consumption plans for residents as well as more flexible power grid management for electric utility companies, such as Duke Energy and ComEd.

In the field of robust design, most estimation methods for output responses of input factors are based on the response surface methodology (RSM), which makes several assumptions regarding the input data. However, these assumptions may not consistently hold in real-world industrial problems. Recent studies using artificial neural networks (ANNs) indicate that input–output relationships can be effectively estimated without the assumptions mentioned above. The primary objective of this research is to generate a new, robust design dual-response estimation method based on ANNs. First, a second-order functional-link-NN-based robust design estimation approach has been proposed for the process mean and standard deviation (i.e., the dual-response model). Second, the optimal structure of the proposed network is defined based on the Bayesian information criterion. Finally, the estimated response functions of the proposed functional-link-NN-based estimation method are applied and compared with that obtained using the conventional least squares method (LSM)-based RSM. The numerical example results imply that the proposed functional-link-NN-based dual-response robust design estimation model can provide more effective optimal solutions than the LSM-based RSM, according to the expected quality loss criteria.

Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks that may occur during product design and manufacturing phases. However, use of the risk priority number (RPN) in traditional FMEA results in difficulties being encountered with regard to quantification of the degree of risk involved. This study proposes the use of a probabilistic time-dependent FMEA (TD-FMEA) approach to overcome limitations encountered during implementation of traditional FMEA approaches. To this end, the proposed method defines the risk priority metric (RPM) as a priority decision value. RPM corresponds to the product of the expected loss and occurrence rate of the failure-cause. By assuming exponential and case functions for each occurrence and detection time instant, the expected loss corresponding to each failure-cause can be evaluated. Utility of the proposed approach has been described in the light of results obtained via its implementation during an automotive-manufacturing case study performed for the purpose of illustration.

As regulations on the emission of pollutants from combustion systems are further tightened, it is necessary to reduce pollutant species and improve combustion efficiency to completely understand the process in the combustion field. Tunable diode laser absorption tomography (TDLAT) is a powerful tool that can analyze two-dimensional (2D) temperature and species concentration with fast-response and non-contact. In this study, stabilized spectra were implemented using the mean periodic signal technique to enable real-time 2D temperature measurement in harsh conditions. A time series statistical-based verification algorithm was introduced to select an optimal spectral cycle to track 2D reconstruction temperature. The statistical-based verification is based on the Two-sample t test, root mean square error, and time-based Mahalanobis distance, which is a technique for similarity analysis between thermocouple and reconstruction temperature of 18 candidate cycles. As a result, it was observed that the statistical-based TDLAT contribute to improving the accuracy of time series-based 2D temperature measurements.

Failure mode and effect analysis (FMEA) is a structured technique for identifying risks that may occur during a given stage of a system&rsquo, s life cycle. However, the use of the risk priority number (RPN) in traditional FMEA results in difficulties with regard to quantification of the degree of risk in the hierarchical failure structure. This study proposes the use of a hierarchical time-dependent FMEA approach to overcome the limitations encountered during the implementation of traditional FMEA approaches. In place of the RPN, a probabilistic loss model is developed under a hierarchical structure considering the elapsed time from the failure-cause (FC) to the system failure. By assuming exponential and case functions for each occurrence and detection time instant, the expected loss corresponding to each FC can be evaluated. As a result of the practical application of the time-dependent probabilistic model through the numerical example, we could reasonably evaluate the risk from the cause of failure in the hierarchical structure in terms of economic loss.

We propose an intuitive interaction system, which is a part of Cooperative Fire Security System using HARMS (CFS2H), to readily deal with fire in a high-rise building. The interaction system is a bridge connecting human, as an operator, to the whole system. Utilizing a natural language processing (NLP) technology using Microsoft Kinect makes the interaction system intuitive and has human-oriented operations. Human-Agent-Robot-Machine-Sensor (HARMS) provides a distributed network so that the systems are able to communicate with a high-level communication protocol. We established a scenario to verify the interaction system along with the system as a whole. The result of the verification left several technical issues and challenges.