Condition Monitoring Devices Research Articles

Integrasi Artificial Intelegent Berbasis Sistem Operasi Android pada Smart Home

This study explores the integration of Artificial Intelligence (AI) into smart home systems using the Android operating system to enhance security, privacy, efficiency, and user comfort. Key security measures include data encryption, robust authentication methods, sandboxing, and AI integration, specifically leveraging Google Assistant for improved privacy controls. Maintenance strategies for smart homes emphasize energy management, device condition monitoring, and enhanced safety features. AI adaptation to user habits enhances productivity and situational awareness, while Android's role in connecting various IoT devices facilitates remote control and energy-efficient recommendations. Methods such as Eco Android, Greensource, byte-code transformations, and automated energy diagnosis tools aid in optimizing energy use. The comparison between smart and non-smart homes highlights the efficiency and convenience of smart homes despite higher installation costs and potential network issues. The development and deployment of an Android-based application, SafeHause, exemplifies practical implementation, emphasizing end-to-end testing, security updates, and user education. The findings affirm that AI integration with Android significantly improves the smart home experience by enhancing energy optimization, data security, and personalized user interaction. Furthermore, the study discusses future trends in smart home technology, such as the potential for more advanced AI algorithms and machine learning techniques to provide even greater personalization and automation. The importance of regular software updates and the role of user feedback in refining smart home systems are also highlighted, ensuring that these technologies continue to evolve and meet user needs effectively.

Prioritization approach for circuit breakers to equip with condition monitoring devices

Prioritization approach for circuit breakers to equip with condition monitoring devices

Research on Deployment Scheme and Routing Optimization Algorithm of Distribution Cable Condition Monitoring Devices

In order to achieve more effective online monitoring of distribution cables, a deployment scheme of the distribution cable condition monitoring devices based on a wireless sensor network (WSN) has been proposed. The proposed deployment scheme can improve the coverage rate and extend the lifetime of the sensor network. Moreover, the clustering method, node density, and node deployment method of the wireless sensor routing algorithm are improved, and based on isosceles triangle non-uniform deployment, a routing optimization algorithm has been proposed to achieve the balance of the energy consumption of each node in the network. Firstly, according to the energy consumption model of the network, the energy consumption of each cluster is calculated, and then by taking the minimum energy consumption of data transmission between clusters as the constraint condition, the optimal clustering distance of the network is solved. Then, according to the scale of network nodes, the density of routing nodes in each cluster is calculated, and the routing nodes in the cluster are deployed by an isosceles triangle. Finally, according to the cluster head election formula, the routing cluster head nodes in each cluster are selected, and the network data information is transmitted to the base station in a multi-hop manner through the routing cluster head nodes. The simulation results indicated that, compared with the traditional routing optimization algorithms, the proposed algorithm could keep the routing coverage at more than 200% all the time, and can effectively balance the energy consumption of nodes, improve the energy utilization efficiency of the routing nodes, and at least extend the lifetime of the network by two times. Moreover, the longer the cables, the more cost savings and the better the life cycle optimization effect of the proposed scheme. In addition, the proposed scheme can greatly reduce the economic cost of network investment, compared with using the demodulator to obtain monitoring data.

Condition Monitoring and Analysis Method of Smart Substation Equipment Based on Deep Learning in Power Internet of Things

An accurate perception of the state of smart substation equipment is a strong guarantee for the reliable operation of the large power grid. This article proposes using deep learning for the device condition monitoring and analysis method in a power internet of things cloud edge collaboration mode. The speeded up robust features (SURF) feature detector is used at the edge of the network to accurately collect the interest points from the image data set, providing a reliable and complete sample data set support for the cloud-based deep learning network. Adding the attention mechanism module to the cloud improves the Yolov5 network model, enhance feature extraction, and increase the monitoring and analysis capabilities of the equipment. The simulation results show that the proposed method has achieved a recall rate of 91.21% and an accuracy rate of 90.54% for insulator fault evaluation indicators.

Simulation Analysis and Online Monitoring of Suspension Frame of Maglev Train

A maglev train is a new type of high-speed railway transportation. A high-temperature superconducting (HTS) maglev system is one of the typical representatives in the field of maglev trains. In order to research the levitation characteristics of an HTS maglev train, the force characteristics and operation performance of the train were researched. However, at present, there is still no research that simulates the real load situation of a running maglev train while analyzing the suspension frame. Regarding this issue, in this paper, the suspension frame is simulated and analyzed to simulate the real load situation of a maglev train. The results show that the suspension frame of an HTS maglev train can bear corresponding loads under different working conditions, and its strength and stiffness meet the requirements of use, safety, and reliability. Random irregularity in the permanent magnet track of an HTS maglev train route is inevitable, which will make the suspension frame produce a vibration response under different running speeds of the train. Vibration of the suspension frame of the train is inevitable. For the electromagnetic levitation (EMS) train, researchers considered monitoring the train status. However, at present, there is no suspension frame condition monitoring device for an HTS maglev train. In addition, the levitation height of the suspension frame affects the suspension performance and traction performance of the vehicle, and the vibration of the suspension frame during running affects the dynamic performance and safety of the suspension frame and the vehicle. Regarding the issue above, through a suspension frame monitoring system, the lateral and vertical vibration acceleration and levitation height of the suspension frame are monitored at different train speeds. The maximum value of vibration acceleration and the fluctuation range of levitation height are within the safe range. It is verified that the simulation analysis of the suspension frame of an HTS maglev train is correct, the suspension frame is safe, and the train can run safely.

Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique

Condition monitoring of wind turbines is progressively increasing to maintain the continuity of clean energy supply to power grids. This issue is of great importance since it prevents wind turbines from failing and overheating, as most wind turbines with doubly fed induction generators (DFIG) are overheated due to faults in generator bearings. Bearing fault detection has become a main topic targeting the optimum operation, unscheduled downtime, and maintenance cost of turbine generators. Wind turbines are equipped with condition monitoring devices. However, effective and reliable fault detection still faces significant difficulties. As the majority of health monitoring techniques are primarily focused on a single operating condition, they are unable to effectively determine the health condition of turbines, which results in unwanted downtimes. New and reliable strategies for data analysis were incorporated into this research, given the large amount and variety of data. The development of a new model of the temperature of the DFIG bearing versus wind speed to identify false alarms is the key innovation of this work. This research aims to analyze the parameters for condition monitoring of DFIG bearings using SCADA data for k-means clustering training. The variables of k are obtained by the elbow method that revealed three classes of k (k = 0, 1, and 2). Box plot visualization is used to quantify data points. The average rotation speed and average temperature measurement of the DFIG bearings are found to be primary indicators to characterize normal or irregular operating conditions. In order to evaluate the performance of the clustering model, an analysis of the assessment indices is also executed. The ultimate goal of the study is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues.

Underwater hybrid energy harvesting based on TENG-MTEG for self-powered marine mammal condition monitoring system

To effectively protect endangered marine mammals, one needs to collect and monitor their biological data and living environment information. This is however a great challenge for the endurance of marine mammal condition monitoring devices. The present study is to propose a self-powered marine mammal condition monitoring system, based on the hybrid energy harvesting mode of triboelectric nanogenerator (TENG) and micro thermoelectric generator (MTEG), to address the challenge. This system not only can obtain energy from the movement of marine mammals but also harvest energy from the temperature difference between the body surface and the ambient. The system fittingly integrates oscillating-TENG (O-TENG), MTEG, and power management unit. Note that O-TENG and MTEG can produce the maximal open circuit voltages of 5.85 V and 1.821 V, respectively. The TENG + MTEG hybrid energy harvesting device can improve the energy collection and battery charging performance by 4.93% compared with the MTEG single energy acquisition device. Hence, it can prolong the service life of the device, achieve an excellent stability, and is easy to manufacture. Eventually, the system can provide an effective and long-term endurance strategy for the condition monitoring device of endangered marine mammals.

Generation of in-Group Asset Condition Data for Power System Reliability Studies

In a power system, unlike some critical and standalone assets that are equipped with condition monitoring devices, the conditions of most regular in-group assets are acquired through periodic inspection work. Due to their large quantities, significant amount of manual inspection effort and sometimes data management issues, it is not uncommon to see the asset condition data in a target study area becomes unavailable or incomplete. Lack of asset condition data undermines the reliability assessment work. This paper tackles this important problem from an unconventional way it explores how to generate numerical and non-numerical asset condition data based on condition degradation, condition correlation and categorical distribution models. Empirical knowledge from human experts can also be incorporated in the modeling process. Also, a probabilistic diversification step can be taken to make the generated numerical condition data probabilistic. This method can generate close-to-real asset condition data and has been validated systematically based on two public datasets. An area reliability assessment example based on cables is given to demonstrate the usefulness of this method and its generated data. This method can also be used to conveniently generate hypothetical asset condition data for research purposes.

Smart Electrical Devices Control with Intrusion Detection Alert

The consumption of electrical energy always increases every year where one of the causes is lifestyle. A bad lifestyle such as forgetting to turn off the unused electronic devices when they are leaving home is one of the factors for increasing consumption of electrical energy which results in waste. This can occur because the device control system is still done manually. One solution to solve this problem is to implement a remote device control system. Several studies have developed a remote-control system but it is not equipped with features that can determine the condition of the device in normal or damaged conditions, so it is difficult to identify the success of the control. In this paper, an Internet of Things-based electronic device control system has been designed and built which is equipped with a device condition monitoring feature. Light, temperature, thermal and electric current sensors are embedded in the system to determine the condition of the device. The control test on the system was successful with an average response time of 3.32 seconds and the accuracy of sensor readings above 89%. With the implementation of this system, users can easily monitor and control electronic devices anywhere and anytime so that the opportunities for efficient and effective use of electrical energy can be further increased.

THE IDENTIFICATION AND SELECTION OF AN OPTIMISED MAINTENANCE STRATEGY FOR CONVEYOR SYSTEMS USED IN THE TRANSPORTATION OF BULK MATERIALS: A CASE STUDY

In recent years an increased number of conveyor failures has been experienced in a port in South Africa, which has directly impacted revenue through unachieved handling volumes. This research aimed to use conveyor failure data to: 1) review and identify the effects of the existing maintenance approach, 2) highlight failure causes and consequences, and 3) determine the most suitable optimised maintenance strategy for conveyors that would reduce failures, thereby reducing downtime and loss of revenue. The results indicated that the current maintenance initiatives were directly linked to conveyor the failures and to cargo being changed without a design verification of the infrastructure. The research has provided useful insights that could lead to fault-finding activities with equipment that is not achieving its design life and, in particular, conveyor belting that has been failing prematurely. Further investigations are required to verify the design of the existing infrastructure in handling the changed cargoes. In addition, the research has shown that condition-monitoring devices will aid in co-ordinating maintenance actions and responses. It also recommended that training plans be updated to ensure that staff are up-to-date with existing maintenance practices before they use the optimised maintenance approach. In summary, this research study has provided essential results and recommendations to warrant the use of an optimised maintenance strategy on conveyor systems, particularly in the port of concern. Keywords maintenance strategy, FMCA, failure analysis

Physically Flexible Ultralow-Power Wireless Sensor

The key challenges of local sensor networks are in supporting high sensor density, information security, physical size, and especially energy efficiency at a level that could eliminate the need for batteries or external power supplies. This article presents a novel scheme that answers all issues at the cost of minor information losses in low data rate applications that tolerate latency. Experimental verification is made using a sensor node implemented on a flexible electronics platform. Lightly encrypted data is transmitted by embedding it into Bluetooth advertising packets, contributing to ultra-low-energy wireless power consumption, and theoretically enabling an unlimited number of nodes in the local network. In the experiments the energy dissipation per transmitted 14 bytes information packet varied between 19.83 μW and 105.93 μW depending on the system configuration, while the data loss rates ranged from 7.4 % to 0.004 %, respectively. As the flexible substrate can be attached to various surfaces, the applications extend from wearable to industrial condition monitoring devices.

Intrusiveness of Power Device Condition Monitoring Methods: Introducing Figures of Merit for Condition Monitoring

Power electronics is becoming pervasive in electricity-based applications, making power device reliability an increasingly important topic. Maintenance operations are necessary to ensure the functioning of power electronic apparatuses <xref ref-type="bibr" rid="ref1">[1]</xref>. Guessing the time for maintenance operations is challenging due to the related tradeoff between maintenance costs and reliability improvements. Lifetime prediction techniques and condition monitoring methods (CMMs) are useful tools for determining the actual need <xref ref-type="bibr" rid="ref2">[2]</xref>.

Research on condition monitoring device of HDTSE for intelligent maintenance

The role of HDTSE is becoming increasingly important. According to the operation characteristics of, This paper analyze the causes of escalator failure, adopts Internet of things, intelligent sensing and distributed technology, presents an escalator operation monitoring system architecture for intelligent maintenance, design the hardware and software of the monitoring device, realize the real-time monitoring of key parameters of escalator and fault warning. It has high application value.

Self-Powered Wireless Sensor Using a Pressure Fluctuation Energy Harvester.

Condition monitoring devices in hydraulic systems that use batteries or require wired infrastructure have drawbacks that affect their installation, maintenance costs, and deployment flexibility. Energy harvesting technologies can serve as an alternative power supply for system loads, eliminating batteries and wiring requirements. Despite the interest in pressure fluctuation energy harvesters, few studies consider end-to-end implementations, especially for cases with low-amplitude pressure fluctuations. This generates a research gap regarding the practical amount of energy available to the load under these conditions, as well as interface circuit requirements and techniques for efficient energy conversion. In this paper, we present a self-powered sensor that integrates an energy harvester and a wireless sensing system. The energy harvester converts pressure fluctuations in hydraulic systems into electrical energy using an acoustic resonator, a piezoelectric stack, and an interface circuit. The prototype wireless sensor consists of an industrial pressure sensor and a low-power Bluetooth System-on-chip that samples and wirelessly transmits pressure data. We present a subsystem analysis and a full system implementation that considers hydraulic systems with pressure fluctuation amplitudes of less than 1 bar and frequencies of less than 300 Hz. The study examines the frequency response of the energy harvester, the performance of the interface circuit, and the advantages of using an active power improvement unit adapted for piezoelectric stacks. We show that the interface circuit used improves the performance of the energy harvester compared to previous similar studies, showing more power generation compared to the standard interface. Experimental measurements show that the self-powered sensor system can start up by harvesting energy from pressure fluctuations with amplitudes starting at 0.2 bar at 200 Hz. It can also sample and transmit sensor data at a rate of 100 Hz at 0.7 bar at 200 Hz. The system is implemented with off-the-shelf circuits.

Research on Thermal Insulation Measures of Power Equipment Condition Monitoring Device in Cold Regions

Outdoor winter temperatures in other northern regions such as Heilongjiang Province are relatively low. As an important equipment of ensuring the safety of transmission lines, the stability of the condition monitoring devices is facing great challenges. In order to ensure that the condition monitoring device can operate reliably in winter, so as to avoid the influence of low temperature in winter on the reliability of the condition monitoring device, in this paper a device is designed that uses solar heat for heat preservation for power equipment condition monitoring device. The device uses an all-glass vacuum solar collector tube to convert solar energy into heat, and realizes instant feedback and control of temperature through a bimetallic temperature sensor. After testing, in winter, the internal temperature of the heat insulation box can be increased by about 30°C compared with the outside, which can effectively improve the winter operating environment of the device.

Tool condition monitoring of PCB milling machine based on vibration analysis

In recent years, specialized machinery processing technology focuses precision, high-speed, and high-performance, Which makes micro-fabrication as an essential factor such as like printed circuit board (PCB). It has smaller aperture, which tends to be extreme due to vibration. The vibration has negative impacts on machining accuracy as well as roughness of the surface will increase the abrasion of the tool and thereby accelerate the decimation of material fatigue. Perforations are made in many ways, such as laser micro-machining including micro-drilling. The cost and the technological consideration in the industry adopt micro drill piercing method. In micro machining applications tool condition monitoring devices are essential. In certain cases device safety is determined indirectly by measuring and evaluating parameters of the cutting operation. In that sense, the main challenge lies in choosing processing parameters and its processing techniques, so that a robust and accurate evaluation of the health tool is achieved. This work involves vibration signal-based analysis to estimate the tool condition of a PCB milling machine's high-velocity micromachining process. In this project a micro-tool condition monitoring system composed of spindle accelerometers is used to measure vibration. In addition, outcome of the analysis from acquired vibration signals improvises life of the instruments.

Power Source Based on Electric Field Energy Harvesting for Monitoring Devices of High-Voltage Transmission Line

This article proposes a power source based on alternating electric field energy harvesting, which can be used for the condition monitoring devices for a high-voltage (HV) transmission line. The energy-harvesting ability of online and offline harvesters depending on different sizes and installation positions is studied herein through a finite-element simulation. The power source uses a novel energy conversion circuit with a cascaded ladder-type topology that can multiply the power when increasing the number of cascaded stages. We set up an HV experiment platform to test the performances of the energy harvester and the power source. The energy-harvesting ability of the harvester approaches the simulation results. The tested power source with four cascaded stages demonstrates a harvesting ability of 620 μW/μA, making it superior to the reported solutions in previous studies. The proposed circuit shows the latent capability of realizing a higher power density.

Embedded Temperature and Anti-Icing Monitoring Systems Directly Printed on 3D Shaped Substrates

The field of machine health and system monitoring has gained interest in different application fields, e.g., monitoring of lubricants, vibrations, surroundings, environmental conditions or the temperature of critical machine parts. This work presents two different condition monitoring devices, which are fabricated in a low cost printing process directly on the non-planar surface of the object to be monitored. The first system is used for thermal condition monitoring, where temperature monitoring is performed by 24 thermocouples resulting in a 24 pixel temperature image of the sample. The thermocouples are printed in an array arrangement, which enables the reduction of the required electrical connections from 48 to 25. The second device is intended for the use in aerodynamic systems, e.g., airplane wings, rotor blades of wind turbines, and helicopters where icing is a critical issue. The aerodynamic lift of aircraft is critically influenced by deposits on the wing or rotor blade surface. Also in other rotor blade systems, e.g., wind turbines the efficiency is drastically dropped and in worst case the icing can cause the failure of the system. The aim is the fabrication of an active monitoring system which is able to detect and also remove the ice from the monitored surface. To this end the system is directly fabricated on the 3D shaped substrate like a wing or rotor blade and the icing is detected using a capacitive sensor and a thermocouple sensor array. In case of icing a heater removes the critical ice deposit.

ECG signal classification with binarized convolutional neural network

Arrhythmias are a group of common conditions associated with irregular heart rhythms. Some of these conditions, for instance, atrial fibrillation (AF), might develop into serious syndromes if not treated in time. Therefore, for high-risk patients, early detection of arrhythmias is crucial. In this study, we propose employing deep convolutional neural network (CNN)-based algorithms for real-time arrhythmia detection. We first build a full-precision deep convolutional network model. With our proposed construction, we are able to achieve state-of-the-art level performance on the PhysioNet/CinC AF Classification Challenge 2017 dataset with our full-precision model. It is desirable to employ models with low computing resource requirements. It has been shown that a binarized model requires much less computing power and memory space than a full-precision model. We proceed to verify the feasibility of binarization in our neural network model. Network binarization can cause significant model performance degradation. Therefore, we propose employing a full-precision model as the teacher to regularize the training of the binarized model through knowledge distillation. With our proposed approach, we observe that network binarization only causes a small performance loss (the F1 score decreases from 0.88 to 0.87 for the validation set). Given that binarized convolutional networks can achieve favorable model performance while dramatically reducing computing cost, they are ideal for deployment on long-term cardiac condition monitoring devices. (Source code is available at https://github.com/yangfansun/bnn-ecg).

Analysis on the Selection of Low Temperature Resistant Materials for Electric Equipment State Monitoring Device in Cold Areas

As the important device to perceive the operating state of grid, the condition monitoring device of electric equipment solves such problems as the high rate of false alarm and failure of device caused by the low temperature in winter of cold areas. This paper measures the thermal conductivity of such three kinds of materials as asbestos, rubber and nano porous aerogels in the temperature range of -55°C to +80°C by steady state method, and analyzes the influence of temperature on the thermal conductivity of three materials. According to the result of experiment, the thermal conductivity of nano porous aerogels is the lowest and is affected least by temperature among three kinds of materials. Nano porous aerogels apply to state monitoring device operated in cold areas; and rubber can also be used as thermal insulation material when the operating temperature is generally higher than -20°C.