CBM and PDM based Maintenance for Higher Equipment Uptimes

Improve equipment uptime with Condition-Based Monitoring (CBM) and Predictive Maintenance (PDM). Integrate data from industrial equipment into IIoT systems, and use AI and machine learning for timely maintenance, reducing downtime and extending equipment life, leading to higher profitability.

CBM and PDM based Maintenance for Higher Equipment Uptimes

Rapid industrialization along withdigitalization of the industries is trending and is the need of the hour to outstandthe growing competition. Maximum production with finite resource is one of thechallenges that every industry is facing. So, to overcome this, digitalization,and automation of the plant process with the help of IIoT (Industrial Internetof Things) 4.0 is being adopted by industries as it provides excellent solutionto their various challenges. As the production depends on the plantperformance, proper functioning of the equipment and machineries with leastdowntime is the ultimate goal of the industries. Achieving this is possible byadopting IIoT that includes multiple modules and functionality.

The benefits of condition-basedmonitoring can be understood by knowing what exactly it is. So basically, thedata from the equipment or the machinery in the plant is integrated to the IIoTthrough various protocols. The data received is analysed using the Artificialintelligence or Machine learning and then various graphs and dashboards arecreated that helps in analysing the condition of the equipment depending on thestored values or thresholds. If it doesn’t satisfy the condition, then anotification is created in the form of alerts through Sms, email or other meansas required by the industry. Knowing the real time data and condition of theequipment helps in providing on-time maintenance that will reduce the downtimeof the machinery. In case condition-based monitoring is not done, you get toknow about the condition of equipment either when maintenance is done or whenbreak down occurs that may affect the productivity of the industry.

Similarly, IIoT provides thepredictive maintenance to the industries that increases the equipment life andreduces the downtime. Based on the analysis through algorithms with the help ofAI and ML, it provides the prediction of either the required maintenance or thefailure before its occurrence. This information proves to be beneficial as itmay avoid any mishap from occurring that includes risk to human life or thelife of the equipment.

So, condition monitoring andpredictive maintenance not only reduces the down time but also increases theefficiency and the production of the industry. Additionally, it increases theequipment life and reduces the risk to manpower working in the area. All theabove benefits results in the increased benefits and profits to the industry.Let’s understand the condition based monitoring and predictive maintenancethrough a use case study or a live example of solution provided by Rubus IIoT4.0 to the Kokusai Pulp & Paper Co., Ltd. Japan. Basically,we have provided a digital transformation and solutions to the Biomass Power plantand integrated the data from various equipments that are Boiler, Turbine,Generator, Vibration sensor, Cooling Tower and steam process.

For Boiler, we have setthe maximum, minimum and the normal value of the pressure as well astemperature, if the value exceeds or goes below the minimum value then alertsare created. All these are done through deep learning and rules-based approachusing AI. Also, dashboards and reports are created for the parameters such asPlant Overview, Boiler, Boiler process, turbine, Generator, Predictions, andvibration sensors. These dashboards and reports gives the idea of plantperformance and help in increasing the efficiency of the plant.

Each dashboard consists ofvarious tags depending on various parameters of the equipment.  For example, the plant overview dashboardmonitors the tags such as main steam pressure control, main stream temperature,main stream volume, Generator active power, received active power, chip supplyfeeder controller, Constant control on generating power, etc. all the factorsthat affect the overall plant performance. Similarly, multiple tags aremonitored for each equipment in the plant and the data is analysed to createdashboards and reports. Furthermore, this data can be used for creating alertsand performing the predictive maintenance.

So, condition-basedmonitoring, not just monitors the plant but also informs you about the futureequipment maintenance and failure through alerts. Whereas predictivemaintenance analyses the historical data and identify the patterns to detectthe anomalies before they actually happen and help in decreasing the downtimeof the equipment. Hence adopting such newer technologies will prove tobeneficial in terms of production efficiency, equipment life, therebyincreasing the profits and standing different from the competitors by providingexcellent service to the customers.