Most businesses constantly face an issue while analyzing whether their critical manufacturing systems are performing to their full capacity while ensuring a consistent reduction in the cost of maintenance. Causes of potential concerns need to be identified early to help organizations come up with more cost-effective plans.
This is where predictive analysis fits the bill. Predictive analysis is used to predict if an in-house machine will malfunction or work correctly. Predictive analysis also helps to plan maintenance in advance, predict failures, classify failure types and recommend necessary actions to be taken after a system fails. The scope of data science is vast and predictive analysis only helps in proving that further.
Factors influencing the success of predictive maintenance
There are three factors that influence if a predictive model is going to be successful or not:
Having the right data
One of the most crucial factors influencing predictive maintenance is having enough data that helps analyze factors that may lead to failure. Additional system features like operating conditions, technical properties also need to be taken into consideration. Additionally, it is also important to make an inventory that will help note the kinds of failure that can occur, and which are the ones that can be predicted.
If at all there is a failure, what the failure process might look like. Having the right data for predictive maintenance also helps understand which parts of the system may have failed and how improvement in terms of performance can be brought about. A system has a vast life span of over a couple of years, which means data collection needs to be done over a couple of years to ensure correct statistics are taken into consideration. A basic data science course will teach you everything about data collection methods.
Framing a predictive maintenance model
The next step is to decide the best modeling strategy for the collected data and how it can lead to the desired output. While there are always multiple modeling strategies to choose from, a predictive maintenance framing strategy should keep a couple of things in mind:
Desired output for the model
Quantity of data collected
Measurements required to predict is a system will succeed or fail
Advance time to predict before a system fails
Setting performance targets for the model such as accuracy, precision and more
Evaluating predictions in predictive maintenance model
A predictive maintenance model predicts whether a system will succeed or fail, what are the conditions under which it might fail and how to ensure that it runs smoothly amongst others. After a predictive model is built, it gets highly essential to analyze how accurate the predictions have been, under what circumstances has the model been able to predict a certain failure or success conditions, and what can be done to combat the same.
Usage of data science in predictive maintenance
Using data science in building predictive maintenance models goes a long way and has its own set of advantages. Here is a lowdown of the ways in which data science has proved to be beneficial for the same:
Minimizing the cost of maintenance
Data science helps understand when to repair a system or machine and prevents unnecessary expenditures by predicting how frequently maintenance should be done.
Root cause analysis
Data science digs deeper into the causes of high failures and understands why systems malfunction occasionally. It also helps suppliers deal with the potential supply of materials accordingly.
Reduce unnecessary downtime
Predictive maintenance is required to predict if an ad when systems might malfunction. A prior data science analysis only helps in lessening the risk of unforeseen disasters.
Efficient planning for smooth operations
Data science ensures that there is no time wasted in fixing systems that are not vital or replacing equipment that has no usage. This way it plans labor efficiently and also ensures that the operations of the business run smooth.
A course in predictive maintenance and building models is an interesting choice for professionals enthusiastic in pursuing a data science career.