Intelligent motor provides enhanced diagnostics and control for next generation manufacturing systems (original) (raw)
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Conference Record of 1996 Annual Pulp and Paper Industry Technical Conference
Motor failures are very costly in a typical paper mill. Typical motor failure cost upwards of 20000perhour.MotorrepaircostsfortheNorthAmericanmillsofChampionInternationaltotalover20 000 per hour. Motor repair costs for the North American mills of Champion International total over 20000perhour.MotorrepaircostsfortheNorthAmericanmillsofChampionInternationaltotalover10 000 000 per year. Lost production associated with these failures can reach a staggering $1 000 000 per day. Considering these facts, Champion International embarked on a program to use intelligent motor control and protection equipment in all new installations, where applicable, and to replace outdated motor protection equipment. The combination of advanced motor protection, flexible control, and communication provided Champion with the opportunity to directly control and monitor their processes. The advanced features of the new equipment provide warning of impending problems, minimize downtime, and improve process results. This paper provides case histories where the new equipment replaced dated electromechanical protection and control equipment. In addition, we also discuss the present generation of motor protection technologies and the need for a new generation of motor protection equipment that truly provides total motor protection against all known motor failure modes.
IJERT-Add-On Unit for Multi-Parameter Monitoring of any Motor using IOT
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/add-on-unit-for-multi-parameter-monitoring-of-any-motor-using-iot https://www.ijert.org/research/add-on-unit-for-multi-parameter-monitoring-of-any-motor-using-iot-IJERTV10IS070134.pdf In today's world the need of the hour is atomization, remote monitoring, quick data acquisition and failure analysis which brings efficiency in asset management. It is difficult, to keep track of the different motor by days long manual observation. Electrical motors and drives consumes about 45% of the power generation. However, if the electrical machines are not maintained properly the motors consumes about 5% to 10 % of excess power, which affects the productivity and revenue. Monitoring of parameters of the different types of motors is crucial which continuously operates large number of electrical drives. Keeping this in mind, the present approach has been made to apply the advantages of wireless communication and programming of embedded technology towards monitoring the multi parameters of different motors using GSM. In the present work, the machine parameters like current, voltage, speed, temperature, direction and status are measured using the sensors for continuous monitoring. The data is collected and processed, and transferred to a remote server wirelessly. Any deviation from the safe operating conditions is reported.
A step forward on intelligent factories: A Smart Sensor-oriented approach
Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA), 2014
Sensors always played a significant role on the industrial domain, since monitoring the current machine's process state is notoriously an advantage for shop-floor analysis, and consequently, to rapidly take action according to the production system demands [3, 6, 7, 8]. The I-RAMP3 European Project explores exactly these demands, and proposes new approaches to efficiently address some of the nowadays difficulties of the European Industry. The Smart Sensor technology is explored in the I-RAMP3 Project using the NETwork-enabled DEVice (NETDEV) concept, as a logical entity for equipment encapsulation with high level of communication capabilities and intelligent functionalities. Therefore, not only how the NETDEV concept is implemented, but also how to use sensors is explored in the present paper, being means of UPnP Technology, for communication extensibility, and PlugSense Framework for easy sensor integration and complexity addition. Moreover, the importance of sensors based on the context of the I-RAMP3 is explored, discussing some trends and possible steps to be taken in conventional production towards the next generation of Smart Manufacturing Systems.
Intelligent Sensor Modes Enable a New Generation of Machinery Diagnostics and Prognostics
Compelling economic, competitive, and technological factors are changing the way many companies view machinery maintenance, repair, and overhaul (MRO) activities. This shift toward a new Maintenance Management Paradigm has implications in many areas of the business including manufacturing scheduling, control, finance, inventory, quality, and asset management. Implementation of the new Maintenance Management Paradigm will require three fundamental building blocks. First, is a framework that enables the efficient re-use of best-in-class diagnostic and prognostic software, hardware, and sensor modules. An open-system architecture will be fundamental to meeting this objective. Second, is the ability to rapidly deploy needed hardware and software elements in a reliable and cost-effective manner across distributed system components. Wireless, intelligent sensor nodes will play an important role in the deployment of future systems. And third, is the infrastructure and that will permit system level integration of an ensemble of distributed intelligent system elements to develop actionable diagnostic and prognostic information. Higher-level diagnostic and prognostic information will drive critical decision making to insure maximum system reliability, lowest operating cost, maximum revenue generation or mission success for example. This paper provides specific examples of elements in the areas of Framework, Distributed Intelligent Modules, and Infrastructure for system-level integration.
Low-cost real-time monitoring of electric motors for the Industry 4.0
Procedia Manufacturing, 2020
Predictive maintenance of industrial equipment has become a critical aspect in the Industry 4.0. This paper shows the design, implementation and testing of an Industrial Internet of Things (IIoT) system designed to monitor electric motors in real-time. This system will be the basis for detection of operating anomalies and a future predictive maintenance system. The system has been designed using low-cost hardware components (wireless multi-sensor modules and single-board computer as gateway), open-source software and a free version of an IoT analytics service in the cloud, where all the relevant information is stored. The module gathers real-time data about the vibrations and temperature of an electric motor. Vibration analysis in the temporal and frequency domains was carried out. Furthermore, analysis in the frequency domain was carried out both in the module and in the gateway to compare their capabilities. This approach is also the springboard to take advantage of edge and fog computing as a complement to cloud computing. The prototype has been tested in a laboratory and in an industrial dairy plant.
IOT BASED ELECTRICAL MOTOR CONTROL AND MONITORING
At present, industries are rapidly shifting towards automation. Today's industrial automations mostly based on programmable controllers and robots. In order to do the tedious work and to serve the mankind, automation is developed in industries. DC motor plays an important role in various industries hence we selected it. This project present a system to provide protection, control and monitoring the condition of DC motor. Here Arduino Uno and various sensors like current, voltage, speed and temperature sensors are used. Real time values of various parameters like current, voltage, temperature and speed can be monitored in ThingSpeak mobile app. By continuous monitoring, the motor can be protected against fault like short circuits, overloading, overheating etc. hence machine performance is improved.
Self-Diagnosis and Automatic Configuration of Smart Components in Advanced Manufacturing Systems
2015
One of the key elements for the next generation of Intelligent Manufacturing is the capability of self-diagnosis, where the machinery used can itself report any breakdown or malfunction based on data, and self-reconfiguration as a way to improve responsiveness in case of sudden requirement changes, either by customer request or production line downtime. All these capabilities allow for quicker and improved systems reliability, leveraging the critical production phases as ramp-up, scheduled and unscheduled maintenance. Based on these premises, the main intent of the project Intelligent Reconfigurable Machines for Smart Plug&Produce Production (I-RAMP) is to develop innovative concepts such as NETworkenabled DEVices (NETDEVs) acting as a technological shell to all the shop-floor equipment, converting it into an agent-like system and tackling the existing gaps between hardware and software for improving the European Industry. Keywords—Wireless Sensor Networks; Intelligent Systems; Manu...
International Journal of Distributed Sensor Networks, 2013
This paper presents a theoretical study for verifying the impact of using smart nodes in motor monitoring systems in industrial environments employing Wireless Sensor Networks (WSNs). Structured cabling and sensor deployment are usually more expensive than the cost of the sensors themselves. Besides the high cost, the wired approach offers little flexibility, making the network deployment and maintenance a complex process. In this context, wireless networks present a number of advantages compared to wired networks as, for example, the ease and speed of deployment and maintenance and the associated low cost. However, WSNs have several limitations, such as the low bandwidth and unreliability, especially in harsh environments (e.g., industrial plants). This paper presents a theoretical study on the performance of WSNs for motor monitoring applications in industrial environments, taking into account WSNs' characteristics (i.e., unreliability and communication and processing latency)...
Real Time Condition Monitoring System for Industrial Motors
— In generally, predictive maintenance of induction motors is well suited for small to larger scale industries in order to reduce downtime, increase efficiency and reliability. In this paper, the vibration and temperature of the induction motor is analyzed in order to gather specific information that can predict motor's bearing failure. Well analyzed vibration signal easily shows the difference between the running operation of the healthy and faulty motor. Using IoT, this paper shows Real Time Condition Monitoring System for Industrial Motors.