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Rapid industrialization along with digitalization of the industries is trending and is the need of the hour to outstand the growing competition. Maximum production with finite resource is one of the challenges that every industry is facing. So, to overcome this, digitalization, and automation of the plant process with the help of IIoT (Industrial Internet of Things) 4.0 is being adopted by industries as it provides excellent solution to their various challenges. As the production depends on the plant performance, proper functioning of the equipment and machineries with least downtime is the ultimate goal of the industries. Achieving this is possible by adopting IIoT that includes multiple modules and functionality.
The benefits of condition-based monitoring can be understood by knowing what exactly it is. So basically, the data from the equipment or the machinery in the plant is integrated to the IIoT through various protocols. The data received is analyzed using the Artificial intelligence or Machine learning and then various graphs and dashboards are created that helps in analyzing the condition of the equipment depending on the stored values or thresholds. If it doesn’t satisfy the condition, then a notification is created in the form of alerts through SMS, email or other means as required by the industry. Knowing the real time data and condition of the equipment helps in providing on-time maintenance that will reduce the downtime of the machinery. In case condition-based monitoring is not done, you get to know about the condition of equipment either when maintenance is done or when break down occurs that may affect the productivity of the industry.
Similarly, IIoT provides the predictive maintenance to the industries that increases the equipment life and reduces the downtime. Based on the analysis through algorithms with the help of AI and ML, it provides the prediction of either the required maintenance or the failure before its occurrence. This information proves to be beneficial as it may avoid any mishap from occurring that includes risk to human life or the life of the equipment.
So, condition monitoring and predictive maintenance not only reduces the down time but also increases the efficiency and the production of the industry. Additionally, it increases the equipment life and reduces the risk to manpower working in the area. All the above benefits results in the increased benefits and profits to the industry. Let’s understand the condition based monitoring and predictive maintenance through a use case study or a live example of solution provided by Rubus IIoT 4.0 to the Kokusai Pulp & Paper Co., Ltd. Japan. Basically, we have provided a digital transformation and solutions to the Biomass Power plant and integrated the data from various equipment's that are Boiler, Turbine, Generator, Vibration sensor, Cooling Tower and steam process.
For Boiler, we have set the maximum, minimum and the normal value of the pressure as well as temperature, if the value exceeds or goes below the minimum value then alerts are created. All these are done through deep learning and rules-based approach using AI. Also, dashboards and reports are created for the parameters such as Plant Overview, Boiler, Boiler process, turbine, Generator, Predictions, and vibration sensors. These dashboards and reports gives the idea of plant performance and help in increasing the efficiency of the plant.
Each dashboard consists of various tags depending on various parameters of the equipment. For example, the plant overview dashboard monitors the tags such as main steam pressure control, main stream temperature, main stream volume, Generator active power, received active power, chip supply feeder controller, Constant control on generating power, etc. all the factors that affect the overall plant performance. Similarly, multiple tags are monitored for each equipment in the plant and the data is analyzed to created dashboards and reports. Furthermore, this data can be used for creating alerts and performing the predictive maintenance.
So, condition-based monitoring, not just monitors the plant but also informs you about the future equipment maintenance and failure through alerts. Whereas predictive maintenance analyses the historical data and identify the patterns to detect the anomalies before they actually happen and help in decreasing the downtime of the equipment. Hence adopting such newer technologies will prove to beneficial in terms of production efficiency, equipment life, thereby increasing the profits and standing different from the competitors by providing excellent service to the customers.
