Data Analytics in Aircraft Maintenance: From Insights to Action
Data is everywhere, and aviation is no exception. Modern aircraft generate an enormous amount of data during every flight, from engine performance statistics to in-flight sensor readings. This data can be a goldmine of information, providing insights that can enhance safety, efficiency, and maintenance. Now we need to turn this huge amount of data into actionable information and insights and data analytics is the solution. So let’s dive deeper into this topic.
What is data analytics?
Data analytics is the process of examining data sets to find trends and draw conclusions about the information they contain. It's a diverse field that uses various analysis methods, such as math, statistics, and computer science, to gain insights from data. Data analytics covers everything from basic data analysis to thinking about how to collect data and creating the structures to store it. For example, predictive maintenance is a specific application of data analytics within the broader field, using analytical techniques to forecast equipment failures and proactively manage maintenance activities.
Benefits of applying data analytics
Predictive Maintenance
The prediction of potential equipment failures by analyzing historical data and identifying patterns. This proactive approach enables timely maintenance interventions, reducing unscheduled downtime and enhancing overall aircraft reliability.
Cost Reduction
By predicting maintenance needs and optimizing repair schedules, airlines can minimize operational disruptions and lower maintenance costs. Additionally, data analytics can help optimize the procurement of spare parts, reducing inventory costs.
Improved Safety
Analyzing data from various sources, including sensor data and maintenance records, can enhance safety by identifying potential issues before they become critical. This proactive approach contributes to the overall safety and reliability of aircraft operations.
Efficiency and Performance Optimization
Data analytics can be used to analyze aircraft performance data, leading to improvements in fuel efficiency and operational performance. This optimization contributes to cost savings and a more environmentally friendly operation.
Enhanced Decision-Making
Access to real-time data and insights allows maintenance and engineering teams to make informed decisions swiftly. This agility is crucial in addressing operational challenges, improving efficiency, reducing costs and ensuring compliance with safety regulations.
Extended Asset Lifespan
Through data-driven insights, operators can better understand the lifespan and performance of critical components. This knowledge facilitates strategic decisions about component replacement or refurbishment, ultimately extending the overall lifespan of aircraft assets.
Regulatory Compliance
Data analytics can help ensure compliance with aviation regulations by providing accurate and up-to-date records of maintenance activities. This is crucial for meeting regulatory standards and maintaining a strong safety record.
Customer Satisfaction
Last but not least reliable aircraft operations, fewer delays, and improved safety contribute to a better overall passenger experience. This, in turn, enhances customer satisfaction and loyalty.
What do you need?
Data analytics relies on a variety of tools and technologies. Here are some of the key components you need to think of:
Sensors and Data Collection
Modern aircraft have numerous sensors that collect data on everything from engine performance to cabin conditions. These sensors provide the raw data that forms the basis of analytics.
Data Storage
The massive amount of data generated by an aircraft requires sophisticated data storage solutions. This often involves cloud-based systems that can handle vast amounts of data.
Data Processing and Analysis
Data analytics software and algorithms are used to process and analyze the data. Machine learning and artificial intelligence play a crucial role in identifying patterns and anomalies within the data.
Visualization Tools
Data is only useful if it can be understood. Data visualization tools help turn complex data into easily digestible charts and graphs, making it easier for aviation professionals to interpret the information.
Challenges
While the potential benefits of data analytics are substantial, here are three major challenges we need to address:
Data Quality
One rather important point to be considered to benefit from using data analytics is data quality and data standardization! We cannot emphasize it enough the term garbage in garbage out; you cannot drive value from data and make the right decisions based on flawed data.
A quick example, if you have been using your MRO/M&E system for let’s say 10 years and have a fleet of around 50 aircraft tons of terabytes of data residing in the MRO/M&E system. Every day more data is added to the system. Having your data organized, cleansed, labeled, identifying, and filling the gaps is needed to make proper use of data analytics and predictive maintenance.
Lack of integration of Systems
Related to the above topic of data quality. The lack of system integration poses a significant obstacle to effective data analytics. This issue leads to data silos, hindering a comprehensive view and analysis of information across different departments. Inefficient integration processes result in data latency, impacting the timely availability of real-time or near-real-time data crucial for certain analytics applications.
Lack of Expertise
Harnessing the power of data analytics requires skilled professionals who understand aviation and data analysis. It’s more than making a pie chart in Excel it’s about extracting and cleansing data, setting data standards, creating, and managing a data warehouse, and in the end presenting this data in a logical cognitive matter. Either airlines need to invest in training and recruitment to bridge this gap or they need to find the right industry partner to bridge this gap.
Where to start?
As with everything you need to start somewhere:
1. The basis
Define Objectives and Scope
You cannot solve everything at once. As for every other project, set goals and objectives, asking what is of importance for the department to focus on the most significant issues first. In that way, everyone has the same goal in mind, and work is done accordingly, and no one gets lost in the huge amounts of data.
Assemble a Cross-Functional Team
If you decide to do everything in-house project this is a rather important step. Form a team with expertise in data science, aviation maintenance, and IT. Ensure representation from key stakeholders of the area you want to focus on, including engineers, analysts, and decision-makers.
2. The Data
· Data Collection and Integration
Identify the relevant data sources, including maintenance logs, sensor data, and historical records. Establish data integration processes to bring together diverse datasets for comprehensive analysis.
· Data Cleaning and Preprocessing
Cleanse and preprocess the data to handle missing values, outliers, and inconsistencies. Ensure data quality and integrity for accurate analysis.
3. The Technology
· Tools and Model Development
Select appropriate analytics tools and technologies based on the nature of the data and project requirements. Develop statistical or machine learning models tailored to predict maintenance needs, identify trends, or optimize processes. Test and refine models to ensure accuracy and reliability.
· Visualization
Choose a visualization tool that is suitable for presenting data in a clear and meaningful way. Create user-friendly dashboards that provide a comprehensive overview based on your objectives and models. Ensure that the design facilitates easy interpretation of trends, patterns, and critical information.
· Documentation and Knowledge Sharing
Document the analytics process, models, and findings for future reference. Facilitate knowledge sharing within the team and across the organization.
4. Training and Development
· Train Personnel
Provide training to maintenance staff on the use of analytics tools and interpreting insights. Foster a data-driven culture within the maintenance department.
· Monitor and Evaluate
Implement monitoring mechanisms to track the performance of analytics models. Regularly evaluate the effectiveness of the analytics solution in meeting predefined objectives. Identify opportunities for improvement and refine the analytics process accordingly.
Working with a Supplier
As mentioned in the part about the challenges often the lack of expertise is a huge obstacle in the success of data analytics initiatives. Next to that, more than often time and also resource availability are a constraint, as the running operation always will be first. Here one should think of working with an external supplier that is specialized in this field and should use off-the-shelf software solutions such as NEXUS and AVILYTICS.
