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While many companies look to completely outsource machine learning to an expert venture studio, organizations that have achieved strong results with their own internal machine learning teams can still benefit from outsourcing.
This was the case when NineTwoThree worked with a client with a complex hydraulic system to create a predictive maintenance system. The organization already had the monitoring and data collection infrastructure in place and they turned to NineTwoThree to reach a higher level of AI maturity and create a preventative machine learning-based system with a high level of accuracy.
When a single component malfunction can disrupt an entire hydraulic system, detecting faults before they become larger issues is critical. The most common maintenance strategies are:
The benefits of predictive maintenance are clear from a cost, performance, and safety perspective. In terms of improved ROI, predictive maintenance increases the lifespan of machines, ensures equipment is running more efficiently and reduces waste. Avoiding unexpected component failures keeps those parts from negatively impacting other parts of the machine and from threatening operator safety.
Our client was aware of the risks associated with unexpected downtime and the benefits of predictive maintenance. They had a complex hydraulic system with a primary working and secondary cooling-filtration circuit, with both circuits connected via the oil tank. They reached out to us to create a machine learning model which could predict the degradation of specific components, including the cooler, valve, pump, and hydraulic accumulator.
The data science team working on the hydraulic system had already installed condition monitoring sensors and gathered a dataset with labeled degradation levels for each separate component. They reached out to NineTwoThree to develop the machine learning model to make use of this data.
Many organizations that are able to reach a level of AI maturity and data readiness on their own will then reach out to a venture studio like NineTwoThree to take them to the next level of expertise. In our experience, some of the reasons an organization will outsource machine learning include:
Predictive maintenance projects can become extremely complex when dealing with a large amount of data, creating accurate predictions for degradation, and developing machine learning models.
Data collection and classification usually make up a large portion of any machine learning project. Luckily, our hydraulic system client provided a raw dataset based on their many physical and virtual sensors. The physical sensors measured parameters during the load cycle of the hydraulic system. The virtual sensors used heterogeneous physical sensors to combine types of data to compute a measurement and generate information to get the observed synergistic effects.
With all the sensors and load cycles, we ended up with a massive total of 43,680 features. After an exploratory analysis, we also found that many of the sensors were highly correlated, which could lead to a multicollinearity problem.
Dealing with a large number of features can create additional complexities when building predictive maintenance models. One challenge is overfitting. When dealing with real-life production data, an overfitted model is unlikely to prove as effective.
Using NineTwoThree’s expertise in building predictive models, we were able to reduce the number of features from 43,680 to a more manageable and easier-to-analyze 136.
Once all the sensor data is organized, we worked to develop machine learning models to predict degradation levels for each component of the hydraulic system. Understanding which models to use, and which hypotheses to test, can save a lot of time and training and help achieve greater model accuracy quicker.
For this project, we use four machine learning models to predict the degradation of each of the main components - cooler condition, valve condition, internal pump leakage, and hydraulic accumulator.
Our initial goal for this project was to achieve accuracy above 95%. The four machine learning models we developed achieved accuracies of 99.0% or better.
NineTwoThree’s decades of machine learning experience helped reach high levels of accuracy and to avoid costly and time-consuming challenges. We were able to quickly identify problems and workshop solutions to our clients' data challenges and efficiently develop ML models with the best chance of achieving success.
Now, when any of the models identify the inefficient performance of a component, the client’s maintenance experts receive an immediate notification of the problem. This alert provides data on exactly which component of the hydraulic system requires their attention and what the issue may be. This allows for intelligent, preventative maintenance to limit downtime, reduce maintenance costs, and extend the life of their equipment.
Whether a company already has a high degree of data maturity, or they are just becoming aware of the benefits of ML for manufacturing, working with experienced ML developers can make a huge difference.
If you’re looking to get started with predictive maintenance, reach out to NineTwoThree. We have a strong background in manufacturing and offer machine learning consulting and software solutions to make the most of predictive maintenance. Our team even holds IoT sensor patents from our past work in manufacturing.