How can you use IoT sensors to monitor machine health?

Our platform is being used in various industries and OEMs are providing various data points using various sensors to help monitor the health of the machines and generate new insights based on the environment they are operating in. This data helps keep the machines in production for much longer and lowers downtime hence, a direct impact on the OEE. Companies must consider not only sensors but the application development process to make sure that the applications being developed are extensible, and scalable (to other product types) and cater to different customer types (account management). AEPs like our Flex83 platform are providing a path to a viable digital transformation journey and quicker ROI.

IoT sensors can be any device capable of sending a digital signal from the inputs they are designed to receive. The hardware required and global infrastructure to exchange information with IoT sensors is readily available. The challenge facing any company ambitious to provide machine learning (ML) insights is in gathering enough contextual information to interprate those signals. The development of decision-making algorithms designed to interpret the signals require clear definitions of 'normal', and well-defined criteria to determine whether the signals require corrective action. Defining the context of a signal is fundamental if machine generated insights or predictions are to have any integrity.

I never thought a Transformer can be monitored using IOT sensors until I joined an Electrical MNC. In a world where every machine can be monitored and controlled, why not a electrical system?! We use different sensors to monitor temperature, humidity, climate, hydrogen, etc in electrical equipment's and this information will be further pushed to cloud from where we provide our customers with reports on health of machine, pattern in which machine is working, alarms if any value is recorded beyond threshold etc. Digitization of electrical systems results in energy efficiency which helps in achieving Sustainability. IOT platforms enables the same.

Think beyond the machine itself. Yes IoT sensors can help to enable preventative maintenance, to reduce downtime, and to identify issues. However you don’t operate machines for the sake of it. You operate them to produce an output. IoT monitoring of machinery forms part of your quality control process, your cash flow management, your supply chain and so on. Knowing how your manufacturing operations are performing at a given point in time enables you to have greater control and confidence in up stream and downstream processes.

We used IoT technology on tow equipment to provide comprehensive log and status indicators, according to below: 1- 1.5MW Jenbacher generator for monitoring performance and status. It is also utilized by the manufacturer for remote troubleshooting and technical support. 2- in remote oil well variable speed units to transmit alarms and status as well as operational parameters trends.

IOT sensors can be utilized in machines for their different parameter controls such as tonnage, correct tooling, pressure, temperature, etc. and process data monitoring for machine health and product performance. It can also be used as poke-yokes in different functionality to prevent machines breakdowns and product non-conformance along with added benefits of data availability for analysis before (preventive maintenance) or after (reactive maintenance) malfunction.

IoT sensors to monitor machine health is a great technology led evolution in the industrial and manufacturing sectors. IoT sensors can provide real-time data on various parameters, enabling proactive maintenance, reducing downtime, and optimizing overall equipment efficiency. IoT sensors combined with Machine Learning can also help organisation detect patterns and anomilies in the sensors. Trigger alerts when abnormal conditions are detected. Machine Learning can be handful for predictive maintenance i.e. using historical data to predict when maintenance is likely to be needed. Scheduling maintenance activities proactively, minimizing downtime and unexpected failures.

Os sensores com tecnologia IoT podem agregar muito a confiabilidade de equipamentos e sistemas. Através de diferentes leituras, como por exemplo: press?o, temperatura, vibra??o, e etc. Os sensores podem atuar de maneira preditiva, informando o nível de desgaste e quanto tempo levará para a quebra, proporcionando assim evitar gastos desnecessários com manuten??o preventiva, n?o necessitando de algum técnico realizando inspe??es in loco, e garantindo confiabilidade. Com a somatórias destes aspectos, trazendo ganhos financeiros significativos. Hoje existem tecnologias para motores por exemplo, onde através de um sensor sem fio, monitora condi??es do motor que envia para um aplicativo, podendo assim intervir quando houver necessidade.

Regularly review the performance of the IoT system and its impact on machine health monitoring. Use feedback and data insights to make continuous improvements in the monitoring strategy and system configuration.

Using IoT and edge analytic is one of a key tools to tackle predictive manitanance principles in asset management. In comparision with traditional methodes (sensors, IO card, PLC and data processing) is advantage in flexibility and cost of deployement. Typical values used in predictive approach are temperature, vibration, sound, oil viscosity... dont forgot to utilise power monitoring as a improtant source of data. Increase of current value can indicate lack of lubrication or worn components. Tricky part (and where lot of effort is aplied) is definition of limits for allerts. Any hint how to preceed there on most efficient way?

1- Identificar sensor compatível para leitura das grandezas desejadas; 2- Range das leituras desejadas; 3- Compatibilidade com sistema a ser integrado; 4- Grau de prote??o de acordo com o ambiente instalado; 5- Dimens?es e peso de acordo com a necessidade de instala??o;

Improving machine health hinges on selecting IoT sensors that align with key performance indicators (KPIs). Vibration sensors can predict and prevent failures, while temperature and pressure sensors fine-tune operational processes for better efficiency. Monitoring digital inputs and outputs, like valve statuses, provides essential insights. Choosing the right sensors is crucial for proactive maintenance and enhanced machine longevity.

RTFM… long time ago someone told me. I agree with above written. IMHO, talk to the vendor who will be installing and maintaining your Automation platform, they would give you the best sensors that would work with the platform. After all, you need more than just a few datapoints.

No mercado existem sensores IoT para monitorar condi??es de maquinas, onde através de software de inteligência artificial, trabalha de forma preditiva informando o grau de desgaste e quanto tempo ha até que o desgaste se torne uma falha e/ou uma quebra.

Same way one uses none IoT sensors. Keep in mind, large motors and wireless data transfer do not work very well. There is a reason why power and data cable tend to be laid far apart.

Sensor fusion is needed to get actionable insights here. Now just a vibration sensor or temperature sensors, but link it with camera images and the possibility to get actionable insights get better

In most of the industries, the scenario goes directly where everything is manual entry every amc every Maintance , combination of live sensor data with maintenance history will provide a invaluable insight on the machine health predictive maintenance

Sensor is a data collection point… what firmware updates??? The most simple one is made of two dissimilar metal strips and read with your multimeter. It can do that for decades without the need of software update.