What are the challenges in Manufacturing Industries?
Technical faults in machinery will decrease turbines' productivity, disrupt deliverables, and ultimately decrease credibility. Several turbines are available for a single farm, so it isn't easy to manage them manually because we have several components for each turbine. If one major component stop functioning and service are not provided on time, it will affect the whole production. So we need to take care of each component, alerts generated by them, and when was last service done. Therefore IoT (
Internet of Things) in Manufacturing Industries is necessary.
By using our IoT devices and
AI-based models, we will analyze Manufacturing Industries.
Our historical data will determine production in each and predict the incoming production year.
Thus the customers can see when the last service like generator bearing replacement, generator replacement took place, and days spent after the last service.
This will also help to determine the total expenses on each turbine. If those particular expenses reach the threshold still productivity is growing down. This means we need to be attentive to other components. There might be chances for the damage of some other major parts.
Using historical data, we need to forecast the failure of components to provide service in advance through the AI model and IoT sensors used.
Description of the Deployed Dashboard
Described below are the benefits of our production analytics dashboard.
Production by Month: This graph will predict the production through turbines in each month.
Production by Time: This graph will represent the maximum production and achieved target.
Farms Service and Production Analysis: This graph will represent after replacing every turbine's components, what will be the effect on the production of every turbine?. This also tells if a turbine is crossing the threshold value of that particular replacing component.
Expenses on farms: This graph will represent each turbine's expenses if it exceeds the targeted value, then we have to pay attention to the other components.
End Customer Value
Main Value Addition: Based on a variable description of the business KPIs.
Description: Here, it will help the customer know about turbines' performance at different times and dates and production status monthly. Through this, customers will reduce technician service visits, increase the main bearing's life. By identifying turbines with high/low risk of grease problems, saving turbine material cost, increased Production Avoiding Id extended damage and reducing fines.
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