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Recent studies indicate that because of downtime industrial manufacturers lose an estimated $50 billion each year. For example, in auto manufacturing, downtime can cost a staggering $1.3 million per hour, according to reports.
So, if you are an owner of a business that relies heavily on machinery, and you are not doing any predictive maintenance yet, chances are you are suffering from downtime losses even while reading this blog post.
So how can Big Data analytics help you prevent downtime expenditures and how to do it right? We’ve talked with our Big Data and Data Science specialists that are currently working on predictive maintenance projects and are willing to share the insights with you.
Why you can’t neglect predictive maintenance
First of all, let’s see why you shouldn’t underestimate predictive maintenance and how Big Data analytics can help you predict equipment failures and reduce downtime costs.
Many manufacturing business owners say it’s a real challenge to predict when you need to service the equipment and it’s difficult to weigh the risks of lost production time against those of a potential breakdown.
They traditionally address the problem in two ways: either reactively (fixing the already existing failures) or proactively (using past experience to anticipate potential breakdowns). Unfortunately, these approaches aren’t effective enough.
If you don’t predict precisely that a machine or a piece of equipment breaks down, downtime may be really long, as you don’t only need to replace a failed part but you may also need to order it and ship it overseas. That results in stalling production, and downtime costs will naturally soar.
ATM faults (failures) are a frequently occurring problem for banks. The key problem is to predict that an ATM cash withdrawal transaction gets interrupted due to a paper jam or part failure in the cash dispenser. Thus, the ATM can be repaired and serviced proactively, and the machine won’t fail midway through a transaction.
Wind turbine failures. A key component in wind turbines is the generator motor. If it fails, it is extremely expensive to fix. Big Data analytics can help the energy companies prevent turbine failures and ensure minimal downtime. Also, using predictive models companies provide insights into different factors that contribute to the failure, which helps technicians better understand the major causes of problems.
Transportation and logistics
Wheel failures cause half of all train derailments and cost billions to the overall rail industry. Also, wheel failures cause rails deterioration, premature rail breaks, and derailment accidents. To avoid that, railways can use predictive maintenance to monitor the performance of wheels and replace them in a preventive manner.
Big Data Analytics is even more critical in industries like aviation when during the flight there is no physical access to the device and the testing environment, and tech faults can’t be fixed by the cabin crew. What’s more, only major airports provide maintenance teams. And predicting major faults goes a long way and can save billions of dollars.
Aircraft engine parts failure
Aircraft engine parts replacements are among the most critical maintenance problems in aviation, and Big Data Analytics here is a very helpful tool.
Flight delay and cancellations
Tech hitches that are not serviced in time may cause canceling of flights and deranging of scheduling.
Maintenance of in-flight equipment
Data Science is used for the anticipation of the lifetime of a device and predicting when it should be replaced or repaired. What’s more, Big Data Analytics and Data Science help to find interconnection between different factors. For example, a specific type of equipment may perform differently depending on a specific type of a plane, and its location in a certain part of the aircraft.
How to do Big Data Analytics for predictive maintenance
To illustrate how to do it, we are using our experience in implementing predictive maintenance in aviation. Our client, Gogo, a global leader in in-flight connectivity needed a qualified engineering team to undertake a complete transition of Gogo solutions to the cloud, build a unified data platform, streamline the system of predicting failures.
To perform quality Big Data Analytics we need two core components: building a Big Data ecosystem and applying the most effective combination of Data Science models to the collected and processed data.
Big Data engineering as the foundation of Big Data analytics
The most substantial part of any data science project comes down to building an orchestrated ecosystem of platforms that collect siloed data from different sources.
Before using any algorithms, you first of all need to have the data structured, aggregated, and cleaned up. Only then, you can turn that data into actionable insights. In fact, ETL (extracting, transforming, and loading) and further cleaning of the data constitute around 70% of most data science projects. That’s why many businesses encounter a problem of finding Big Data experts.
Using Data Science models to fuel predictive maintenance
When you have your Big Data platform up and running, you can drive your business with Data Science predictions.
Data science solution that we have developed for Gogo is an ensemble of models that include Machine Learning classifiers trained with Multiple Instance Learning, Probabilistic Mixture models and regression analysis. Combining all these different learning algorithms together allowed us to obtain overall better performance get more accurate and double checked data as the cost of a mistake is very high. Below is an example of aircraft equipment failure prediction results. Results were provided along with explanations for an engineering team.
The final estimates and predictions impact decision making and choosing which antennas and when should be replaced.
Gogo engineers look through our recommendations, reports, explanations, and alerts and can make well-grounded decisions. Big Data Analytics also helps with logistics issues. If we predict a failure of an antenna in Japan, an antenna can be proactively sent to Japan, which saves time and money.
Replacing one antenna takes up about 8 hours of work. That means 8 hours of downtime and renting an hangar for servicing, which is very expensive. On the other hand, If this is not done, the plane will travel with a faulty antenna for a week or more. Thus, predictive maintenance helps to find time for servicing in-flight equipment at the most suitable time, for example, when there are no flights scheduled for the plane.
Though predictive maintenance is time-consuming and costly, the effort pays off, and it pays off well. If an antenna is wrongly predicted as faulty and it is replaced, a company may incur substantial losses. Leveraging Big Data Analytics, Gogo has achieved around 8o % of the true positive rate. Only one antenna was mistakenly classified as failed out of more than 200 healthy antennas.
Downtime costs industrial manufacturers dozens of millions a year. Going for Big Data Analytics and predictive maintenance may be a great solution for companies that want to anticipate tech failures and slash downtime costs. It is worth noting that Big Data engineering amounts for around 70% of any Data Science project and that’s what businesses need to focus on first of all. Companies that have implemented predictive maintenance have already improved their decision-making and reduced average downtime by more than 50%.
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