Boosting Aircraft Fleet Maintenance with Big Data
Enterprise Condition-Based Maintenance Plus (eCBM+) Interoperability – Phase II
Condition-based maintenance (CBM), a manufacturing industry best practice, effectively reduces a product’s risk of failure while also reducing the cost of maintaining it. By migrating from a maintenance approach based on fixed schedules to one based on demonstrated need, companies in the transportation and energy sectors have maximized the impact of their maintenance efforts while avoiding the waste and loss of productivity that comes from performing unnecessary repairs. However, the amount of data generated by modern systems, whether they be military hardware filled with embedded sensors or smart devices connected to the Internet of Things (IoT), can prove overwhelming to CBM practitioners. As those systems become more complex and interconnected, the difficulty of maintaining them without a robust big data management solution grows.
NCMS is assisting the U.S. Naval Air Systems Command (NAVAIR) with its movement toward CBM Plus (CBM+), a set of practices that builds on CBM by leveraging real-time data collection, transformation, and analysis to inform and augment maintenance activities. NCMS, in partnership with the SAS Institute and the Department of Defense (DoD) CBM+ Action Group, developed an enterprise suite of tools and processes to integrate, store, retrieve, and transform data from numerous disparate NAVAIR sources, while providing a model for the development of private-sector CBM+ tools.
The resulting architecture is compatible with NAVAIR’s existing Hadoop environment, cleans and quality-checks its contents, and boasts advanced analytics capabilities for translated stored data into actionable task information. By arming technicians across government and industry with similar information, CBM+ frameworks can support their work with detailed, real-time feedback and monitoring, making maintenance processes more proactive, cost-effective, and efficient.
The advantage of well-implemented CBM is the reduction of waste caused by the unnecessary repairs that take place as part of schedule-based maintenance. According to IBM, only 18 percent of technical assets have an age-related pattern of failure; repairing most components at regular intervals will necessarily lead to perfectly functional parts being replaced while some parts at risk of failure are overlooked. A transition to CBM therefore significantly increases the efficiency of maintenance activities; IBM reports reductions in downtime and preventative maintenance costs of up to 50 percent. In the case of specific systems, the gains can be even more substantial: A 2014 study published in the Journal of Air Transport Management found that adoption of a CBM approach leads to an 80 percent reduction in maintenance costs for six critical components of the Airbus A320.
The potential of CBM and the challenges of implementing it grow in tandem as IoT devices see wider use. Sensor-enabled IoT devices continually transmit operational data to a wider network, allowing technicians to remotely monitor their status in real time while decreasing the need for physical inspection. At the same time, they greatly increase the volume of data that they must store and analyze.
While data storage and retrieval capabilities have kept pace with technological developments, lagging analytics capacity has become a bottleneck in the process of translating raw data from thousands or tens of thousands of sensors into actionable findings. Consulting firm McKinsey reports that of the 30,000 sensors on an offshore oil rig, data from fewer than one percent is currently used to inform decision-making. This gap leads to delayed reporting or outright data loss, negating the value of IoT devices, improved sensor technology, and real-time feedback.
Like the automotive, aviation, rail, power generation, and heavy equipment industries, the DoD is migrating from traditional maintenance strategies to true-condition-based, real-time, data-driven maintenance. To capture its dual pursuit of the efficiencies offered by CBM and the breadth of awareness provided by big data, DoD employs the term CBM+, describing CBM systems that encompass data and product lifecycle management.
Because of this, the impact of CBM+ systems extend beyond maintenance activities, also comprising the acquisitions and supply chain management processes. NAVAIR maintains a large fleet of data-rich assets, already collecting vast amounts of information from embedded sensors already installed on its platforms; the missing crucial elements were the bandwidth and tools for analysis. To meet this demand, NAVAIR partnered with the DoD CBM+ Action Group, the SAS Institute, and NCMS. The project team, funded through the NCMS Commercial Technologies for Maintenance Activities (CTMA) Program, developed an enterprise suite of analysis tools, processes, and associated metrics to be integrated with the NAVAIR requirements, budgets, and information technology solutions.
By leveraging commercial technologies and best practices, such as Hadoop and associated commodity hardware for warehousing big data, the project team provided NAVAIR with the means to transform, integrate, store, and retrieve vast amounts of data from disparate sources. With that accomplished, the project team shifted its focus to analytics, providing a set of tools that translated that data into relevant, concise findings and action items. As system integration and complexity in military and industrial settings continues to increase, this capability will only gain importance, as maintenance becomes more difficult, time consuming, and costly in the absence of an effective big-data solution.
Key elements of this project included development of an overall architecture, including integration with the data stores of NAVAIR’s closed loop Hadoop environment, metadata management, governance, data cleaning methods, and data quality assessments, as well as development and interpretation of appropriate advanced analytic methods based on specific use cases. The end product was a set of tools with direct applicability to specific maintenance tasks.
- S. Navy – NAVAIR Systems Command HQ, Patuxent River
- OSD-led Joint Service CBM+ Action Group
- SAS Institute, Inc.
- National Center for Manufacturing Sciences (NCMS)
 “Are Your Preventative Maintenance Efforts Wrenching Away Precious Resources? Time to List to Your Machines” (IBM, 2016), https://www.ibm.com/blogs/internet-of-things/wp-content/uploads/2016/05/IG_preventiveMaintenance15_ss.pdf.
 Alberto Regattieri et al., An Innovative Method to Optimize the Maintenance Policies in an Aircraft: General Framework and Case Study, vol. 44, 2015, doi:10.1016/j.jairtraman.2015.02.001.
 James Manyika et al., “The Internet of Things: Mapping the Value Beyond the Hype” (McKinsey Global Institute, June 2015), 25, http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/The%20Internet%20of%20Things%20The%20value%20of%20digitizing%20the%20physical%20world/Unlocking_the_potential_of_the_Internet_of_Things_Executive_summary.ashx.