Automated machine learning is arguably the fastest, most efficient way for aerospace original equipment manufacturers (OEMs) and maintainers to accurately predict when parts will fail and position replacements where needed. This improves operational performance for rotorcraft and fixed-wing fleets while reducing costs for civil and military operators.
Traditional machine learning teaches a computer to isolate valuable data by identifying trends and patterns as part of predictive analysis. Automated machine learning takes these traditional machine learning algorithms and automatically determines which of them best fits your data and use case. Organizations can more fully take advantage of machine learning technology by automating it. For example, in the aerospace industry, automation can speed up the process of identifying and mitigating future component failures exponentially.
How Automation Improves Machine Learning
Today, for the most part, aerospace manufacturers manually develop the machine learning algorithms that help them predict component failures on aircraft. Data scientists assess terabytes of data coming off an aircraft to build an algorithm to accurately predict a failure.
Data scientists must also manually adjust a slew of parameters, called hyperparameters, for the typical machine learning model prior to training. It’s common for the process of model selection, tuning, retuning, and training to take several months of work. With automated machine learning, the choice of your model and tuning of hyperparameters is automatic. The goal is for the automated machine learning engine to run a small sample of data against those models and parameters and then bubble up the best ones to the top. Larger and larger sample sizes train the models, with each iteration reducing the number of models that make it to the next round.
In Sila’s experience, the best automated machine learning software vendors on the market have 1,000 or more built-in models to run data against. The software quickly presents you with a ranking of the top models that best fit your fleet maintenance requirements.
Applying Automated Machine Learning
Automated machine learning works best when there is a significant amount of historical data available to train a model. This is the case for many systems found nose to tail on rotorcraft and fixed-wing aircraft.
While historical data is used to train the model against new, incoming data, the historical data must be relevant to the problem set at hand. If you were trying to predict tomorrow’s next music trend, for example, and all you had was historical data from the 1940s, then the model wouldn’t be very effective.
Automated machine learning benefits business operations by reducing maintenance costs. Components that aren’t repaired or replaced before they fail can easily become a huge cost to the operator, triggering a domino effect that impacts other systems. If you can predict the failure point for a component—the 85 percent to failure point, or whatever point you think is optimal for removing or replacing a part—you can streamline supply chain operations leading to reduced aircraft downtime.
The ability to conduct condition-based maintenance—where parts are fixed or replaced only when performance degrades rather than on a fixed schedule—is a direct application of automated machine learning. It has the potential to generate the necessary algorithms in a much shorter timeframe, achieving maintenance cost reductions sooner rather than later.
Other Matters to Consider
While automated machine learning can significantly accelerate time to value, it is not a cure-all. In Sila’s experience, the rapid iteration afforded by automated machine learning simultaneously introduces governance and security challenges as sensitive data needed for the algorithms may be exposed to a wider audience. Data engineering, the practice of getting data to the right place at the right time in the necessary format, still plays a large role in the success of any data science project.
Automated machine learning also does not handle complex feature engineering, which is the process of introducing additional data to the model based on domain knowledge. Models produced by automated mechanisms should still be checked for accuracy to avoid things like overfitting, which is the inability for a model to generalize well outside of the training data set.
Operationalizing a model has many other considerations. How often should you retrain your model? How do you automate that process? How do you handle deployment and auditing of your model? Keep these things in mind when introducing automated machine learning to ensure your organization makes it past the proof-of-concept phase.
Automated machine learning gives military and civil OEMs and maintainers a tool that offers massive time and cost savings, while improving operational availability of their fleets. For example:
- Automated machine learning reduces the time to value for model building. It enables organizations to get more accurate and relevant models faster because they are operating on fresher data.
- Organizations understandably want to focus on high-cost high-value-part overhaul and maintenance. Automated machine learning means models can be built to cover all parts instead of designing a model for a single part.
- Automated machine learning reduces the cost and time associated with developing an algorithm. Issues thought of as too small to solve can now be addressed.
- Automated machine learning makes the process of machine learning accessible to non-experts, enabling data scientists to focus their efforts where they’re most needed.
- Automated machine learning makes finding people that can work on data science problems easier. It focuses on the data science, shifting the spotlight away from the technical details and allowing managers and analysts to focus on the business problem.
- Don’t forget that automated machine learning only accelerates the model-building time to value. Data and feature engineering, security, governance, and operationalizing models are time-consuming and all-important tasks that are required to make any data science project successful.