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The dream of transitioning to a smart factory simply cannot be realized without condition monitoring. Condition monitoring, or relying on the industrial internet of things (IIoT), enables an organization to do predictive maintenance of machinery. It can prevent a total shutdown of the production process as a result of machine break down by avoiding the break down altogether.
Condition monitoring, as the term states, entails observing a certain parameter of the machinery, say, temperature, and using that to monitor the ‘health’ of the machines. With this data, maintenance work can be scheduled for a time that is convenient and least disruptive to the production process.
With condition monitoring and the generation of data, organizations can not only monitor machinery and diagnose problems in the manufacturing process but also continually improve the performance of the machinery.
When it comes to digital transformation the process there could be differences depending on the type of machines a certain firm uses and their production process. The components of any IT transformation, however, remain the same. Here are the four basic ones.
Sensors are the very foundation of condition monitoring. The data required for condition monitoring such as temperature, humidity, humidity among others are measured or recorded with the help of sensors attached to the components of the machinery. With the help of these sensors, deviations can be easily detected and appropriate action is taken.
Sensors channel out huge amounts of data that could take up a lot of space on the cloud. Prevent this, gateways are necessary for the condition monitoring process. In the gateway, information is analyzed and summarized before being sent to the cloud. Gateways are categorized into field gateways and cloud gateways.
Field gateway collects data and pre-processes it locally by making aggregates and sieving through messages only to forward those that are important to the cloud. This makes data transmission effective. To ensure there is a secure data transmission of data between the field gateways and the cloud, the cloud gateways are used.
All the data collected from the sensors is in its raw form is stored in the data lake until it is required. When a question arises that needs an answer, the data in the lake can be retrieved and analyzed to provide an answer.
Different from the data lake, the data warehouse stores contextual information. Data stored in the data warehouse is analyzed and organized.
Step 2 analytics and user applications
After setting up the initial architecture, the next step is acquiring analytics and user applications.
Predictive maintenance is made possible by the analytics bit of condition monitoring. Data collected from the machine components would be useless without analysis drawn from it. With the help of analytics algorithms, the analysis is communicated to the user and a diagnosis can be drawn. Break down can be prevented too by the help of root cause analysis which helps identify failure triggers and figuring a way of eliminating them to prevent future defects to the system.
To understand the insights derived from the sensors and analysis, user applications are vital. User applications allow interactions between the system and the user. The information gathered is shown in an easily understandable format like charts and graphs. This way, the user can easily detect deviations and take the necessary action.
Step 3: evolve
With condition monitoring, you have to keep making adjustments as your business grow. Technology is always changing and as time goes, you will need to adopt more advanced architecture.
With deep analytics deviations from normal data patterns are flagged. This could be an indication that the machines have underlying issues. The data is fed into machine learning algorithms and a diagnosis is made. After that maintenance is scheduled to prevent total breakdowns. Predictive models can also be used to improve the machines in use by feeding the data collected to the predictive models as it becomes available. Adjustments are then made to develop a better machine physical model.
When it comes to IoT, the system can operate with some level of automation. When a certain level of a certain parameter is exceeded, that can be detrimental to the machine learning model can raise alarm to employees to alert them of a possible breakdown.
With condition monitoring, anomalies that would lead to the production of substandard goods are flagged down and acted upon to ensure that an organization maintains the highest level of consistency when it comes to product quality.
There are a lot of reasons or gains that make it worth it to adopt IoT condition monitoring for your factory. Here are just a few.
Lower maintenance cost
With IoT incorporating machine learning, issues that could cause machine failures are detected and addressed early enough before deteriorating to more serious issues. With predictive maintenance, maintenance can be scheduled at a convenient time without disrupting the production process. This saves the organization time and also cushions against loses caused by machine breakdowns.
Longer machine life.
With proper maintenance being undertaken, the machine can last longer as they are well taken care of. Also, condition monitoring enables you to establish the optimal parameters that a machine can operate on and the limits not to exceed.
Availability of real-time data for improvements
With the machine learning models, a company can learn how to improve the performance of a machine and develop a better model. The data also enables an organization to explore ways of improving the final product’s quality.