Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City

Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed is a concern. This article proposes a layered methodology to enable complex machines with automatic fault detection or predictive maintenance. It presents a layered structure to perform the collection, filtering and extraction of indicators, along with their processing. The aim is to reduce the amount of data to work with, and to optimize them by generating indicators that concentrate the information provided by data. To test its applicability, a prototype of a cash counting machine has been used. With this prototype, different failure cases have been simulated by introducing defective elements. After the extraction of the indicators, using the Kullback–Liebler divergence, it has been possible to visualize the differences between the data associated with normal and failure operation. Subsequently, using a neural network, good results have been obtained, being able to correctly classify the failure in 90% of the cases. The result of this application demonstrates the proper functioning of the proposed approach in complex machines.

On the Conductivity of Proton-Exchange Membranes Based on Multiblock Copolymers of Sulfonated Polysulfone and Polyphenylsulfone: An Experimental and Modeling Study

The effect of relative humidity (RH) and degree of sulfonation (DS) on the ionic conductivity and water uptake of proton-exchange membranes based on sulfonated multiblock copolymers composed of polysulfone (PSU) and polyphenylsulfone (PPSU) is examined experimentally and numerically. Three membranes with a different DS and ion-exchange capacity are analyzed. The heterogeneous structure of the membranes shows a random distribution of sulfonated (hydrophilic) and non-sulfonated (hydrophobic) domains, whose proton conductivity is modeled based on percolation theory. The mesoscopic model solves simplified Nernst–Planck and charge conservation equations on a random cubic network. Good agreement is found between the measured ionic conductivity and water uptake and the model predictions. The ionic conductivity increases with RH due to both the growth of the hydrated volume available for conduction and the decrease of the tortuosity of ionic transport pathways. Moreover, the results show that the ionic conductivity increases nonlinearly with DS, experiencing a strong rise when the DS is varied from 0.45 to 0.70, even though the water uptake of the membranes remains nearly the same. In contrast, the increase of the ionic conductivity between DS=0.70 and DS=0.79 is significantly lower, but the water uptake increases sharply. This is explained by the lack of microphase separation of both copolymer blocks when the DS is exceedingly high. Encouragingly, the copolymer membranes demonstrate a similar performance to Nafion under well hydrated conditions, which can be further optimized by a combination of numerical modeling and experimental characterization to develop new-generation membranes with better properties.

The art of land administration

The article provides recommendations for land administration. With an abundance of productive agricultural land, success in agricultural production and the attractiveness of rural life are achieved by the ability of the population to pay, access to investment resources, the availability of a commodity distribution network, good management, land market infrastructure, a system of information support for agricultural activities, land management, cadastre and land evaluation for making reasonable management decisions and the necessary state support of agriculture.

Is having an educationally diverse social network good for health?

While network research often focuses on social integration as a predictor of health, a less-explored idea is that connections to dissimilar others may benefit well-being. As such, this study investigates whether network diversity is associated with changes in four health outcomes over a 3-year period of time in the U.S.A. Specifically, we focus on how an underexplored measure of network diversity—educational attainment assortativity—is associated with common self-reported outcomes: propensity to exercise, body-mass index, mental health, and physical health. We extend prior research by conducting multilevel analyses using this measure of diversity while adjusting for a range of socio-demographic and network confounders. Data are drawn from a longitudinal probability sample of U.S. adults (n=10.679) in which respondents reported information about themselves and eight possible alters during three yearly surveys (2013–2015). We find, first, that higher educational attainment is associated with more educationally insular networks, while less-educated adults have more educationally diverse networks. Results further suggest that having educationally similar networks is associated with higher body-mass index among the less educated. Further exploration of the relationship between ego network diversity, tie strength, and health is warranted.

Identifying the optimal initial adopters and adoption paths of the internet-based intangible network goods

Purpose Internet-based intangible network good (IING) has undergone rapid developments, even revolutionized multiple industries in recent years. IING is highly dependent on the rapid diffusion rates for development success. For firms, how to select the initial targets or “seeding points” to accelerate the adoption process is critical in network marketing campaigns. The purpose of this study is to provide a new method to identify the optimal initial adopters and adoption paths. Design/methodology/approach First, the author generalize three aspects influencing IING’s adoption, namely, innovation attributes, customer’s personality and word-of-mouth. Next, we establish a modified gravity model to describe how social interactions affect consumer’s adoption behavior. Then, simulate the adoption process by setting each agent as the initial adopter to identify the optimal initial adopters. Finally, trace the information flow to forecast the adoption paths. Findings The model reveals how individual interactions (micro level) aggregate into the diffusion process (macro level). The optimal initial adopters are determined by a combination of factors as follows: IING’s attributes, the adopter’s diffusion ability, the potential-adopter’s personality and the trust degree between adopters and potential-adopters. Among all these factors, trust degree plays a most important role. Originality/value This study proposes the conceptual model of IING’s adoption from a perspective of dyadic influence, in which an adopter’s influence on its peers depends on pairwise characteristics of both parties. The authors propose a new method to identify the optimal initial adopters and adoption paths based on the gravity model. It is the first time to introduce the gravity model to describe IING’s adoption, which is a creative application of social physics. The findings provide new insights in IING’s adoption and identifying the key nodes in networks.

Investigation of Influence of Drill Geometry and Thrust Force During Drilling of Aluminium 2014 Alloy on Burr Formation Through Design of Experiments and Nueral Network

Drilling is a hole making process on machine components at the time of assembly work, which were identify everywhere. In precise applications quality and accuracy play wide role. Now a day’s industries are suffered by cost incurred during deburring, especially in precise assemblies such as aerospace/aircraft body structures, marine works and automobile industries. Burrs produced during drilling causes dimensional errors, jamming of parts and misalignment. Therefore, deburring operation after drilling is often required. Now, reducing burr size is a burning topic. In this study experiments are conducted by choosing various input parameters selected from previous researchers. Effect of alteration of drill geometry on thrust force and burr size of drilled hole investigated as per taguchi design of experiments and found optimum combination of most significant input parameters from ANOVA to get optimum reduction in burr size by design expert software. Drill thrust influences more on burr size, clearance angle of drill bit causes variation in thrust, burr height observed in this study. Compare these output results with neural network software @easy NN plus. Finally, concluded that increasing the number of nodes increases the computational cost and decreases the error in nueral network. Good agreement was shown between the predictive model results and the experimental responses.

PCB access impedances extraction method of in-situ integrated circuit

This article describes an extraction technique of input and output impedances of integrated circuits (ICs) implemented onto the printed circuit boards (PCBs). The feasibility of the technique is illustrated with a proof-of-concept (POC) constituted by two ICs operating in a typically transmitter-receiver (Tx-Rx) circuit. The POC system is assumed composed of three different blocks of emitter signal source, load and interconnect passive network. This latter one is assumed defined by its chain matrix known from its electrical and physical characteristics. The proposed impedance extraction method is elaborated from the given signals at the transmitter output and receiver input. The terminal access impedances are formulated in function of the parameters of the interconnect system chain matrix. The feasibility of the method is checked with a passive circuit constituted by transmission lines driven by voltage source with RL-series network internal impedance and loaded at the output by the RC-parallel network. Good correlation between the access impedance reference and calculated is found.