IoT: Automating Engine Degradation Diagnostics, Optimizing MRO

The duration for which an engine stays grounded depends on the need for maintenance and repair of the engine. Higher the wear and tear, greater is the need for maintenance, longer is the time it stays grounded, bigger is the revenue loss. The question is — how can we minimize this time and optimize an engine performance?

It has been observed that nearly 50 percent of engine delays encountered are caused due to maintenance errors.[1] Almost 12 percent of all aircraft accidents cite maintenance as a contributing factor. [2] There could be many reasons related to the lack of proper maintenance of fleets such as misperception of hazards, time pressures, organizational processes, and in time decision making. By incorporating a data-driven decision-making culture in an organization, processes can be made more efficient and productive.

Take this case of an aero engine manufacturer who optimized their engine degradation diagnostics process using IoT and analytics to deliver better customer experience. The manufacturer wanted to track the performance of their engines, detect wear and tear of its different parts, analyze the cause, and alert the customer in time to schedule its maintenance. The objective was to minimize engine downtime, help customers optimize operations, and extend engine’s life.

A cyber-physical system was installed to monitor key parameters of the engines. A fully developed data model was developed and deployed on the real-time engine data. “Normal” vs. “degrading” parameters were clearly defined in the model to help the tracker distinguish between the two types of signals.

Over time as data started flowing in from the cyber-physical system into the data model, it was able to detect anomalies. These anomalies were analyzed to understand the cause of degradation. Alerts were sent to the management and the maintenance team about the problem and its cause. It helped the management to take the right decision in time, while the maintenance team [1] https://www.maintenanceassistant.com/blog/poor-maintenance-cost-lives/ could carry out specific maintenance procedures. This helped the client save millions in terms of reduced downtime and extended the remaining useful life of the engines. It also improved their organizational and maintenance processes.

A visibility into the maintenance needs of an engine or possible malfunctioning helps companies to better plan their maintenance schedule and operations. It also helps them prevent accidents or a resulting damage to property or lives.

Similar, IoT-based data models can be used by organizations to ensure compliance of different parts of an engine. For example, raw fuel vented out by turbo engines into the atmosphere during a normal engine shutdown should meet certain regulatory requirements. This requirement may change over an engine’s lifetime or there might be changes in the engine’s emission due to maintenance issues leading non-compliance. By tracking the right parameters, comparing, and analyzing the data organizations can ensure compliance.

Following are some of the benefits of automating engine degradation diagnostics:

  • Reduced cost of maintenance
  • In time scheduling of maintenance and repair
  • Visibility into maintenance schedules
  • Better planning of fleet operations
  • Optimized revenue
  • Longer engine life

Please share your experience and views with us here.

Divya Agarwal

Written by Divya Agarwal

on 30 Jan 2017

Divya Agarwal is part of the Global Marketing & Communications team at Quest Global. In her current role, she leads the integrated marketing campaigns for digital solutions, software product engineering and embedded product engineering services. In addition, she manages analyst relations and drives content marketing for building the corporate brand. An engineer and MBA by education, she follows the emerging technology trends keenly and loves to read or cook in her spare time.