{"id":22760,"date":"2023-02-08T12:45:55","date_gmt":"2023-02-08T04:45:55","guid":{"rendered":"https:\/\/orapiasia.com\/?p=22760"},"modified":"2024-01-25T14:00:25","modified_gmt":"2024-01-25T06:00:25","slug":"predictive-maintenance-soars-into-the-future-of-aviation","status":"publish","type":"post","link":"https:\/\/orapiasia.com\/zh\/predictive-maintenance-soars-into-the-future-of-aviation\/","title":{"rendered":"Predictive Maintenance: The Future of Aviation"},"content":{"rendered":"
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"aircraft\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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It is no big news that many industries significantly downsized during the pandemic. However, as the world attempts to recover fully to normal, the aviation industry is facing one of the biggest challenges in finding a replacement for workers retrenched during the pandemic.\u00a0<\/div>\n
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Skilled engineering personnel are particularly in short supply, as many have left for other sectors. Though many other manufacturers, aftermarket providers, and airlines encountered the same acute problem last year, it can be ameliorated to a certain extent through digitalisation. This shift from labour-intensive to capital-intensive\u2014or, more accurately, digital-intensive\u2014can free skilled workers from some repetitive, muscle-powered tasks. Keep reading to discover the game-changing impact of predictive maintenance on the aviation industry and why it’s the future of aircraft upkeep.<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Also read:

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Aviation Cleaning Application Essential Guide<\/a><\/span>
The Force of Attrition in Aircraft Maintenance<\/span><\/span><\/a>
Why Choose an Aircraft Degreaser?<\/u><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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What Is Predictive Maintenance for Aviation?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Predictive maintenance is a strategy for maintaining equipment and systems that use data and analytics to predict when an item is likely to fail. This is in contrast to traditional “reactive” maintenance, in which equipment is only fixed or serviced after it has broken down, or “preventive” maintenance, in which equipment is serviced on a regular schedule, regardless of whether it needs it.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Predictive maintenance aims to reduce the frequency of equipment failures and associated costs, such as downtime and repair costs. Therefore, it is often used in manufacturing, transportation, and energy industries, where equipment failure can be costly and disruptive.<\/span><\/p>\n

To implement predictive maintenance, data is collected from the equipment, such as vibration readings, temperature readings, or oil analysis, which is then analysed in real-time or periodically to detect patterns or anomalies that may indicate an impending failure. By performing maintenance before the equipment actually breaks down, the company can avoid unplanned downtime and reduce overall maintenance costs.<\/span><\/p>\n

It is also commonly used for machine learning algorithms to predict failure based on historical data and utilise IoT sensors to gather data from the machines.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Relationship Between Predictive Maintenance and Chemical 4.0 \n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Chemical 4.0, also known as Industry 4.0, is a term used to describe the integration of advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), into the chemical industry.<\/span><\/p>\n

Predictive maintenance is one aspect of Chemical 4.0, as it uses advanced technologies such as IoT sensors, AI and ML to predict when equipment is likely to fail. These technologies empower automated damage recognition that allows aviation companies to perform equipment maintenance before it breaks down, avoid unscheduled removals, and increase time on the wing. <\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Advantages of Predictive Maintenance<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Predictive maintenance is vital for many reasons.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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  1. Cost savings:<\/span><\/span> By performing maintenance before equipment fails, companies can avoid unplanned downtime and the associated costs, such as lost productivity and repairs. Predictive maintenance can also reduce the overall maintenance cost since the equipment is only serviced when needed.<\/span><\/li>\n
  2. Increased uptime:<\/span><\/span> Predictive maintenance can help increase equipment uptime by identifying and addressing issues before they cause a failure. This can improve productivity and reduce the risk of costly downtime.<\/span><\/li>\n
  3. Safety:<\/span> Predictive maintenance can aid in improving safety by identifying and addressing potential hazards before they cause an accident<\/span><\/li>\n
  4. Equipment optimisation:<\/span><\/span> Predictive maintenance enables optimised equipment performance. Identifying and addressing issues early extends the longevity of the equipment, improves energy efficiency, and reduces emissions.<\/span><\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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    1. Data-driven:<\/span><\/span> Predictive maintenance allows for a more data-driven approach to maintenance, providing insights into how equipment is performing and identifying trends that can be used to improve processes and prolong equipment life.<\/span><\/li>\n
    2. Better decision-making:<\/span><\/span>\u00a0It also allows organisations to make informed decisions about their maintenance activities and budget allocation. This can lead to better overall planning and execution.<\/span><\/li>\n<\/ol>\n

      Overall, it is an essential strategy for maintaining equipment and systems. It can help companies save money, increase uptime, improve safety, optimise equipment performance, and make more informed decisions.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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      Predictive Maintenance Gaining Traction <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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      Industry recovery has spurred interest in predictive maintenance<\/span><\/a><\/span>\u2014with the international border opening and aviation activities returning to normal, and the industry is seeing more airline customers who are keen to incorporate it into their renewed or new maintenance-by-the-hour contract for their critical aircraft components.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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      This may prompt the acceleration of predictive maintenance, which prior to the pandemic, was struggling to gain popularity with many airlines outside of engine applications. However, estimates now show that about 50 airlines are covered by true predictive maintenance, i.e., maintenance following predictions. In addition, many aircrafts have started utilising Original Equipment Manufacturer\u2019s (OEM) predictive maintenance. While these numbers are not as impressive as one would like them to be, they still present a stark improvement from the pre-pandemic era.\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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      Hurdles to Predictive Maintenance \n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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      The lack of data sharing has been one of the major hurdles to the adoption of the new technology. Data sharing refers to the exchange of information between organisations or entities. In this case, airlines need to share data related to the operation of their aircraft with the companies that develop and implement predictive maintenance technology, such as original equipment manufacturers (OEMs) and maintenance, repair, and overhaul (MRO) providers.<\/div>\n
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      However, many airlines have become increasingly protective of their data and guard it more jealously for reasons such as concerns over data security or the perception that the data is too valuable to share.\u00a0<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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      This lack of data sharing can also lead to other issues. For example, in some cases, airlines may not be able to access the operational data they need to use predictive maintenance technology, while at other times, they may be hesitant to share data with third-party MRO providers due to the fear that it would benefit their competitors as much as it would benefit themselves.<\/span><\/p>\n

      By running machine learning algorithms on multiple datasets from various operators, it would be possible to develop algorithms without having direct access to another operator’s data. This will allow companies to maintain their data security, anonymously use their data, and still develop powerful algorithms that could be used across multiple operators.<\/span><\/p>\n

      For airlines to fully adopt predictive maintenance, they must be willing to make changes to their traditional maintenance processes and replace serviceable components that the algorithms suggest should be replaced, even if they do not appear to be faulty. This is because the algorithms use data to predict when these components are likely to fail. Failure to do so could result in increased costs or downtime later on, as equipment failure is more expensive to fix and disrupts operations more than preventative maintenance. Ultimately, the probability of this change relies on the customer’s openness to adopt innovative solutions and willingness to change their traditional processes if the solutions show valuable results.\u00a0\u00a0<\/span><\/p>\n

      To test the validity of an action, companies have to value predictions by effectively collaborating with each other.\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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