Lifetime Maximization of Wireless Sensor Networks for a Fault Diagnosis System Using LEACH Protocol

The breakdown in a machine created by a fault will greatly affect the plant operation. The frame work of fault diagnosis of machines using machine learning techniques is an established area. Here, the fault diagnosis system is implemented with the help of wireless sensor networks (WSN). Each machine in the plant is fitted with sensors (node) from which the fault diagnosis is carried out. The signals/messages from each node are transmitted to a base station, which acts as a central control unit for the entire plant. Fault diagnosis using WSN in a factory setup has few challenges. The major issue in WSN is the life time of the nodes, as they are situated far away from base station in many plants like thermal power plant, refinery, and petroleum industries. To address the life time of the nodes many researchers have developed many protocols like LEACH, SEP, ERP, HCR, HEED, and PEGASIS. The plants need customisation in terms of choosing suitable algorithms and choosing location of the base station within the plant for better life time of the nodes. This paper presents results of both experimental and simulation studies of a typical plant, where the vibration signals from each machine are acquired and through machine learning techniques the fault diagnosis is performed with the help of wireless sensor networks. For illustrative purpose, a well reported bearing fault diagnosis data set is taken up and fault diagnosis case study was performed from wireless sensor networks point of view (experimental study). Here, at every stage, the computational time is taken as a primary concern which affects the life time of the sensor nodes. Then, the WSN of 18 sensor nodes representing 18 machines with LEACH protocol is simulated in Matlab© to study the life time characteristics of each node while keeping base station at different locations. The life time of different nodes is heavily dependent on the location of the base station. Finding the right location of the base station for a given plant is another contribution of this work

Validation: Is Thy Name Research?

Asian Bioethics Review13288-29

Fault diagnosis of wind turbine bearing using wireless sensor networks

This paper proposes wireless sensor networks to monitor the condition of wind turbines. It addresses lifetime maximization issue of sensor nodes using stable election protocol for a cluster of upto nine wind turbines. This paper presents results of both experimental and simulation studies of a wind turbine plant, in which the vibration signals from each wind turbine are taken and with the help of machine learning technique, the fault diagnosis is done for a plant with wireless sensor networks. An experimental case study is performed from a wireless sensor networks with a well reported wind turbine bearing fault diagnosis data set. The outcome of the study shows that if the number of wind turbines is five for one base station, then the lifetime of the sensor nodes are maximum using MATLAB

Hall-Sensor-Based Position Detection for Quick Reversal of Speed Control in a BLDC Motor Drive System for Industrial Applications

This paper proposes the novel idea of eliminating the front-end converters used indirect current (DC) bus voltage variation, thereby allowing for control of the speed of the brushless direct current (BLDC) motors in the two-quadrant operation of a permanent magnet brushless direct current (PMBLDC) motor, which is required for multiple bi-directional hot roughing steel rolling mills. The first phase of steel rolling, the manufacture of plates, strips etc., using hot slabs from the continuous casting stage, is carried out for thickness reduction, before the same is sent to the finishing mill for further mechanical processing. The hot roughing process involves applying high, compressive pressure, using a hydraulically operated mechanism, through a pair of backup rolls and work rolls for rolling. Overall, the processes consist of multiple passes of forward and reverse rolling at increasing roll speeds. The rolling process was modeled, taking into account parameters like roller dimensions, angle and length of contact, and rolling force, at various temperatures, using actual data obtained from a steel mill. From this data, speed and torque profiles at the motor shaft, covering the entire rolling process, were created. A profile-based feedback controller is proposed for setting the six-pulse inverter frequency and parameters of the pulse width modulated (PWM) waveform for current control, based on Hall sensor position, and the same is implemented for closed loop operation of the brushless direct current motor drive system. The performance enhancement of the two different controllers was also evaluated, during the rolling of 1005 hot rolled (HR) steel, and was taken into consideration in the research analysis. The entire process was simulated in the MATLAB/Simulink platform, and the results verify the suitability of an entire-drive system for industrial steel rolling applications

Context-sec: Balancing Energy Consumption and Security of Mobile Devices

Energy Management is of primary importance in mobile devices due to increasing functionality coupled with rapid battery drain. Research analysis reveals that users differ in context and resource usage patterns, which can be leveraged for power savings. A key challenge lies in providing context-adaptive security in an energy aware manner due to increasing sensitivity of user data and analyzing energy-security trade-offs. Towards this challenge, we model the problem of context-adaptive energy-aware security as a combinatorial optimization problem (Context-Sec). We then prove the decision version of this problem to be NP-Complete via a reduction from a variant of the well known Knapsack problem. We then design three different algorithms to solve a relaxed offline version of Context-Sec. The first algorithm is a pseudo-polynomial dynamic programming (DP) algorithm that computes an allocation with optimal user benefit using recurrence relations. The second algorithm is a greedy heuristic for allocation of security levels based on user benefit per unit of power consumption for each level. Finally, the third algorithm is a Fully Polynomial Time Approximation Scheme (FPTAS) for the problem which is has a polynomial time execution complexity as opposed to the pseudo-polynomial DP based approach. We subsequently implement and test the three algorithms on a real-world smartphone usage and wireless networks data-set to compare their performances. To the best of our knowledge, this is the first work that is focused on modeling, design, implementation and experimental performance analysis of any algorithm for context-adaptive energy-aware security. We believe our results will be useful for researchers and practitioners working in this area

Evidence for a hyper-reductive redox in a sub-set of heart failure patients

Abstract Background Oxidative stress has been linked to heart failure (HF) in humans. Antioxidant-based treatments are often ineffective. Therefore, we hypothesize that some of the HF patients might have a reductive stress (RS) condition. Investigating RS-related mechanisms will aid in personalized optimization of redox homeostasis for better outcomes among HF patients. Methods Blood samples were collected from HF patients (n = 54) and healthy controls (n = 42) and serum was immediately preserved in − 80 °C for redox analysis. Malondialdehyde (MDA; lipid peroxidation) levels by HPLC, reduced glutathione (GSH) and its redox ratio (GSH/GSSG) using enzymatic-recycling assay in the serum of HF patients were measured. Further, the activities of key antioxidant enzymes were analyzed by UV–Vis spectrophotometry. Non-invasive echocardiography was used to relate circulating redox status with cardiac function and remodeling. Results The circulatory redox state (GSH/MDA ratio) was used to stratify the HF patients into normal redox (NR), hyper-oxidative (HO), and hyper-reductive (HR) groups. While the majority of the HF patients exhibited the HO (42%), 41% of them had a normal redox (NR) state. Surprisingly, a subset of HF patients (17%) belonged to the hyper-reductive group, suggesting a strong implication for RS in the progression of HF. In all the groups of HF patients, SOD, GPx and catalase were significantly increased while GR activity was significantly reduced relative to healthy controls. Furthermore, echocardiography analyses revealed that 55% of HO patients had higher systolic dysfunction while 62.5% of the hyper-reductive patients had higher diastolic dysfunction. Conclusion These results suggest that RS may be associated with HF pathogenesis for a subset of cardiac patients. Thus, stratification of HF patients based on their circulating redox status may serve as a useful prognostic tool to guide clinicians designing personalized antioxidant therapies