|Machine Learning helps on reducing the production downtime|
In the industrial sector, the occurrence of downtime is unavoidable. This tends to interrupt the production workflow. Hence, an effective method to reduce the downtime frequency is by using a “machine learning” approach which is better than manual predictions. Machine learning can track, analyze and predict using a complete dataset relating to the production sector accurately than manual or other available models. By using this method, we can foresee and make predictions about unknown machine failures and take steps to improve the production efficiency. That in turn provides the manufacturer ample time to make solutions to meet the turnaround time set by customers. In a nutshell machine learning is the smartest way to eliminate downtime
Handling downtime using the three “P” approach. When inactivity of machinery occurs in a production unit, then it is dealt with by anyone of the p’s mentioned below:-
Promptly - whenever a machine breaks down irrespective of whether it had been maintained properly or not, the factory must tackle the downtime by taking up the responsibility to fix it promptly for the production to continue as usual. This is the reactive method. This may involve rocketing costs to rectify the machinery which in turn results in financial instability of the firm coupled with receding production.
Planned - certain industries usually have planned maintenance for their machinery. Where the machines are serviced at a period be it yearly/monthly to reduce the possibility of downtime. This is the preventive method. According to this method, a gap exists between two service schemes. There is a chance for a new unplanned worn out of the machinery. Which ends up in disrupting the existing production.
From the above it is clear whether you choose prompt or planned methodology, both would still result in some production downtime. Therefore, the final method mentioned below is probably the smartest factory method one can rely on.
Predicted – using the collected data of the machinery the process of production is monitored. The downtime is effectively eliminated by predicting the machinery’s breakdown days before its actual worn out session. This in turn aids to act in advance to solve the machine’s functioning. Hence, it maintains the proper working conditions in the production sector. This methodology is called machine learning.
How to diminish downtime using machine learning
In an industrial production setup, there would be various machines performing their individual tasks for the overall production of a commodity. Sensors are installed to the machines. These sensors can collect 1kb of data per reading, at the rate of 1,000 readings per second. The collected data can then be stored in the cloud and used by IoT devices for analysis purposes. This helps in minimizing the time required for collecting data’s and their verification when down time occurs. As in the case of manual data collection, it would be hectic to verify and visualize the data and process the error.
Storing the data
The collected data from different machinery are stored in a data warehouse. This is used for planning workflow situations in the industry. By using this data, we can smartly access the production information and can also effectively reduce the risk of downtime.
Segregating the data
The data from the past to present should be collected up to date and grouped as “healthy data '' which is the useful production data and “sick data '' which refers the downtime data. Only if both types of data are properly fed then good predictions can be made by combining the dataset. This aids in finding out the causation of the machine outbreaks by preferably using the sick data. By this, the previous downtime areas can be removed.
Setting a machine learning model
On possessing ample data, the machine learning algorithm is created and can analyze the future patterns of downtime. This can be eliminated by taking preventive measures or carrying out alterations of the machinery if needed for better production output. In addition to this by using the machine learning model, one can be preplanned to take the machine offline and perform necessary maintenance.