Design for Reliability — Process & AI Applications
Have you ever experienced the stress of having your laptop going dead, not to mention issues with your car and any other critical equipment at work or at home? We feel frustrated because when we buy a product, we trust it will operate over its expected lifespan, without failure or degradation.
Reliability engineering serves to predict and prevent those failures. The practical tasks of reliability engineering are identifying potential failure modes and implementing appropriate design, testing, and monitoring processes.
But there is more to it.
Apart from the above daily life examples we quoted that might create a business interruption but are then (maybe too quickly) forgotten - there can be dramatic failures, as we will see later on in the article, that impact tragically people's lives.
And we should not forget, the reliability of products is connected to a core value such as sustainability:
"With the newly proposed Ecodesign for Sustainable Products Regulation, we expect reliability and reparability requirements to be introduced more systematically for all products", states Isabel Lopez-Neira from the sustainability team of the European Consumer Organisation, as quoted by the Joint Research Centre (JRC) of the EU .
Product reliability is a critical factor in customer satisfaction and brand reputation. Customers expect products to perform consistently over time. Failure to meet these expectations can result in lost sales, warranty costs, and damage to a company's reputation. By designing products for reliability, companies can enhance customer trust and loyalty, reduce warranty costs, and establish a positive brand image.
Let's explore this concept.
What Is Design for Reliability?
Prediction of reliability is a fundamental aspect of Design for Reliability (DFR), a key acronym we will use throughout the article. To predict means to estimate the likelihood of failure of a product or system under specific conditions. It involves analyzing the product's design, materials, and expected usage to determine the probability of failure and the potential impact of failure on the product's overall reliability. By predicting potential failure modes, reliability engineers can implement appropriate design solutions to prevent or mitigate these failures.
Design for reliability (DFR) focuses on ensuring the reliability of a product or system throughout the design stage of its lifecycle, starting from the design phase. It involves identifying potential failure modes and addressing them through appropriate design, testing, and monitoring processes.
Design for Reliability is a critical aspect of product development as it helps to make sure the product meets the intended performance requirements and that it operates without failure.
The importance of product reliability has led to an increased focus on DFR in product development. Reliability engineers work closely with product development teams to identify potential failure modes, design appropriate solutions, and validate the design through testing and monitoring. The DFR process involves several steps, including:
- identifying potential failure modes
- designing solutions,
- analyzing the design
- testing & validating the design
- monitoring the product's performance throughout its lifecycle
Design Without Prediction — A Case History
The importance of designing for reliability cannot be overstated. Failing to do so can have serious consequences, ranging from product recalls and lost revenue to injury or even death. In this chapter, we will explore a case history where a product was launched without adequate prediction of reliability and the devastating consequences that followed.
The case we will examine is in fact the tragic Space Shuttle Challenger disaster of 1986.
On January 28th, 1986, the Space Shuttle Challenger exploded just 73 seconds after launch, taking the lives of all seven crew members on board. This was a tragedy of monumental proportions that the author of this article is old enough to remember well.
The investigation revealed a number of factors contributing to the disaster.
As identified by the legendary physicist Richard Feynman , one of the most significant factors was the decision to launch the shuttle despite knowing that there were serious reliability issues with the O-ring seals on the solid rocket boosters (SRBs). These O-ring seals were designed to prevent hot gases from escaping from the joints between the SRB sections during launch, but they were known to be susceptible to failure in cold weather conditions.
In fact, there had been several incidents of O-ring erosion and blow-by prior to the Challenger launch, and engineers at both NASA and the contractor responsible for the SRBs, Morton Thiokol, had expressed concerns about the reliability of the seals. Despite these concerns, the decision was made to launch the Challenger on January 28th, 1986, even with ambient temperatures being well below freezing.
The catastrophic failure of the O-ring seals on the right SRB led to the explosion of the shuttle and the loss of all seven crew members. The investigation revealed that the decision to launch the Challenger was driven by a number of factors, including political pressure, schedule pressures, and a lack of appreciation for the risks involved.
One of the key lessons from the Challenger disaster is the importance of designing for reliability. The O-ring seals on the SRBs were a critical component of the Space Shuttle system, and their failure led to the loss of the vehicle and its crew. This failure could have been avoided if adequate full reliability analysis and the prediction had been carried out during the design and development of the SRBs.
To recall a recurring concept in this article, reliability prediction is a process of analyzing a product or system to identify potential failure modes and their causes and to develop strategies to mitigate or eliminate those failures. This process typically involves a combination of analytical techniques, such as stress analysis with finite elements (FEA) and Failure Mode and Effects Analysis (FMEA), as well as testing and validation.
In the case of the failure reporting the SRB O-ring seals, a prediction could have identified the susceptibility of the seals to failure in cold weather conditions, and strategies could have been developed to mitigate that risk. For example, alternative seal designs or materials could have been explored, or launch criteria could have been established to prevent launches in cold weather conditions.
However, because the importance of designing for reliability was not fully appreciated, and because there was pressure to meet launch schedules, these steps were not taken and as a result, the Challenger disaster occurred, with tragic consequences.
The lessons of the Challenger disaster have had a profound impact on the aerospace industry and on the broader field of engineering. Since that time, there has been a renewed emphasis on designing for reliability and on the importance of robust prediction processes.
Today, reliability prediction is a standard part of the design and development process for most complex engineering systems extending beyond the aerospace industry to automobiles, appliances, and medical devices.
The Space Shuttle Challenger disaster serves as a dramatic example of the consequences that can arise when a product or system is launched without adequate reliability prediction. The failure of the O-ring seals on the SRBs was a critical factor in the disaster, and it could have been avoided if the importance of designing for reliability had been fully appreciated.
Today, the lessons of the Challenger disaster continue to inform the design and development processes in many industries, emphasizing the importance of DFR.
General Considerations on DFR
DFR is a systematic approach to product development that considers reliability at every stage of the process. It involves identifying potential failure modes and their causes, developing strategies to mitigate those failures, and validating the effectiveness of those strategies through testing and analysis.
DFR also involves considering factors such as:
- manufacturing processes
- materials selection
- operational conditions
And all of these can have an impact on product reliability.
In contrast, designing without reliability prediction can lead to a range of negative outcomes. These may include:
- Increased likelihood of product failure. Without adequate reliability prediction, products may be more prone to failure, resulting in increased costs, lost revenue, and damage to reputation.
- Safety risks. Failure of critical components can pose safety risks to users, leading to injury or even death.
- Recalls and litigation. Failure of a product can result in recalls and litigation, which can be costly and damaging to a company's reputation.
- Missed opportunities. Failing to consider reliability early in the design process can lead to missed opportunities for cost savings and performance improvements.
- Reduced customer satisfaction. Products that are unreliable or fail prematurely can lead to reduced customer satisfaction, which can have a negative impact on a company's bottom line.
In contrast to the Challenger disaster, there are many examples of companies that have successfully implemented DFR and reaped benefits.
For example, Toyota is known for its focus on quality and reliability in its automotive products. This has helped the company to build a strong reputation for reliability and customer satisfaction, which has translated into increased market share and revenue.
Another example is the medical device industry, which has seen significant improvements in reliability and safety in recent years as a result of increased focus on DFR. The implementation of rigorous reliability prediction processes has led to the development of safer, more reliable medical devices that have improved patient outcomes and reduced the risk of adverse events.
In conclusion, the Challenger disaster serves as a powerful reminder of the importance of designing for reliability. Failing to consider reliability early in the design process can lead to serious consequences, including product failure, safety risks, recalls, missed opportunities, and reduced customer satisfaction.
Closing on a positive note: implementing DFR can help companies to build a strong reputation for quality and reliability, which can translate into increased market share and revenue. As such, designing for reliability should be an integral part of the product development process for any complex engineering system.
The Importance of Product Reliability in Design
Reliability of design is crucial for ensuring customer satisfaction and the return for the manufacturers is that it reduces warranty costs and protects a company's reputation.
We mentioned at the beginning the examples of a car or a laptop. Performance is overall important, but a reliable product enhances customer trust and loyalty and helps to establish a positive brand image. Additionally, designing for reliability can lead to cost savings for the producers, by reducing warranty claims from customers.
Quality vs Reliability of a Product
Product quality and product reliability are two important factors that determine the overall value of a product. While these terms are often used interchangeably, they refer to two different aspects of a product's performance. While product quality and reliability are closely related, they are not the same thing as we will see in the following. A product can have high quality but still be unreliable if it fails to perform its intended function consistently over time. Similarly, a product can be reliable but have low quality if it does not meet the customer's expectations or needs:
- Product quality refers to the degree to which a product meets the customer's expectations and needs. It includes characteristics such as durability, functionality, ease of use, and aesthetics. A product with high quality is more likely to meet the customer's needs, while a product with low quality may lead to customer dissatisfaction and lost sales.
- On the other hand, product reliability refers to the ability of a product to perform its intended function without failure or breakdown. Reliability testing is the process of evaluating a product's ability to function as intended over a specified period of time, under normal operating conditions. This process involves subjecting the product to various stress factors, such as temperature, humidity, and vibration, to simulate real-world conditions and identify any weaknesses in the product's design or manufacturing.
Reliability block diagrams are a common tool used in reliability engineering to model the reliability of complex systems. These diagrams break down a system into individual components and calculate the overall reliability of the system based on the reliability of each component and their interdependencies.
Of course, it is important to target both high-quality and high-reliability products. This can be achieved through a combination of design, manufacturing and testing processes that ensure the product meets both the customer's needs and performs its intended function consistently over time.
To design for reliability (DFR), product development teams should identify KPIs such as reliability risks and reliability requirements. The latter requirements are the product's expected performance standards, including its expected lifespan, performance under different conditions, and failure rate. The product development team should define these requirements as early as possible in the design process to ensure they are truly incorporated into the design. But what is the DFR process?
The DFR Process
The DFR process involves several steps, including identifying potential failure modes, addressing them through appropriate design solutions, and testing and validating the design. The following are the minimum steps in the reliability assessment process:
The first step in the Design for Reliability process is to identify potential failure modes. This involves identifying the different ways the product can fail, such as through mechanical, electrical, or thermal stresses.
Once potential failure modes have been identified, the next step for the reliability engineer is to design solutions to address them. Design solutions can include selecting appropriate materials, adding redundancy, implementing error-checking mechanisms, or incorporating fail-safe mechanisms.
After designing solutions, the product development uses reliability engineering tools such as reliability techniques such as Failure Mode and Effects Analysis (FMEA) or Fault Tree Analysis (FTA) to identify potential failure points and evaluate the impact of failures on the product's overall reliability.
The design must then be tested under different environmental conditions to meet the reliability requirements. Testing can include environmental testing, accelerated life testing, and stress testing.
While laboratory/physical testing is an essential step in designing for reliability, it is important at this point to notice that modern tools like computer-aided engineering (CAE) and artificial intelligence (AI) can also play a significant role in the process. These tools can help designers and engineers identify potential reliability issues before physical testing even begins:
- CAE software allows designers to simulate the behavior of a product under various conditions, such as temperature, humidity, and vibration. This simulation can reveal potential weaknesses in the design that could lead to reliability issues down the line. By identifying these issues early on, designers can make changes to the design to improve its reliability before physical testing even begins.
- AI prediction can also be used to predict the reliability of a product based on data from similar products or components. By analyzing this data, AI algorithms can identify patterns and predict potential failure modes in a new product. This information can be used to make design changes or prioritize certain areas of the product for testing.
Once testing is complete, the design must be validated to meet the reliability requirements. This involves comparing the actual performance of the product against the expected performance defined in the reliability requirements.
Finally, the product must be monitored throughout its lifecycle to make sure it continues to meet the reliability requirements. This can involve monitoring the product's performance in the field, collecting customer feedback, and conducting periodic reliability testing.
In conclusion, designing for reliability goals is a critical aspect of product development that helps to ensure consistent performance over time. It involves identifying potential failure modes, addressing them through appropriate design solutions, and testing and validating the design to meet reliability requirements. By following the Design for Reliability process, product development teams can design products meeting customer expectations, reduce warranty costs, and protect a company's reputation.
DFR Success Story: 787 Dreamliner
One concrete case history of Design for Reliability is the development of the Boeing 787 Dreamliner . The development of the 787 Dreamliner was a massive undertaking, with Boeing investing over $32 billion in the program. The 787 was a revolutionary design, featuring a composite airframe and advanced avionics that promised to significantly improve fuel efficiency and passenger comfort.
The Project's Challenges
The development of the 787 Dreamliner was not without its challenges. One of the key challenges that Boeing faced was ensuring that the aircraft would be reliable and safe for passengers. To achieve this, Boeing implemented a comprehensive Design for Reliability program.
Engineers at Work
The Design for Reliability program for the 787 Dreamliner began with a thorough analysis of customer requirements and expectations. Boeing worked closely with airlines and other stakeholders to understand their needs and develop a comprehensive set of reliability requirements for the aircraft.
Once the reliability requirements were established, Boeing began the process of designing the aircraft. The design process involved the use of advanced computer-aided design tools (CAD and CAE), which allowed engineers to model and simulate different aspects of the aircraft's performance.
One of the key aspects of the Design for Reliability program was the use of advanced materials and manufacturing techniques. The 787 Dreamliner was the first commercial aircraft to feature a composite airframe. Composites are lightweight, strong, and resistant to corrosion, making them an ideal material for aircraft construction.
However, composites also pose unique challenges in terms of reliability. Boeing conducted extensive testing of the composite materials and developed new manufacturing techniques to ensure the materials were used correctly.
Another key aspect of the Design for Reliability program was the use of advanced avionics. The 787 Dreamliner featured a highly integrated avionics system, which allowed for greater automation and improved safety. The avionics system was designed with redundancy in mind. If one component failed, there would be backup systems in place to ensure the aircraft could still operate safely.
To make sure that the avionics system was reliable, engineers conducted extensive testing of the software and hardware components. The testing process involved simulating a wide range of scenarios, including extreme weather conditions and system failures. This allowed Boeing to identify and address potential system reliability issues before the aircraft was put into service.
In addition to the Design for Reliability program, Boeing also implemented a comprehensive maintenance program for the 787 Dreamliner. The maintenance program was designed to ensure the aircraft remained reliable and safe throughout its service life. The program involved regular inspections and maintenance checks, as well as reliability activities such as the use of advanced diagnostic tools to identify potential issues before they become major problems.
The Design for Reliability program for the 787 Dreamliner was a resounding success. The aircraft has been in service since 2011 and has proven to be reliable and safe for passengers. In fact, the 787 has one of the highest dispatch reliability rates of any commercial aircraft.
The success of the Design for Reliability program for the 787 Dreamliner has led to its adoption by other aircraft manufacturers.
Today, Design for Reliability is a standard part of the aircraft development process, with manufacturers using advanced tools and techniques to ensure their aircraft are reliable and safe for passengers.
Landing to the Conclusion
In conclusion, the development of the Boeing 787 Dreamliner is a concrete case history of Design for Reliability in action. The Design for Reliability program for the 787 was comprehensive and involved the use of advanced materials, manufacturing techniques, and avionics systems to ensure the aircraft was reliable and safe for passengers.
The success of the Design for Reliability program for the 787 has led to its adoption by other aircraft manufacturers, and it is now a standard part of the aircraft development process.
The 787 Dreamliner stands as a true testament to the power of Design for Reliability to improve the reliability and safety of complex systems.
DFR and Artificial Intelligence
As artificial intelligence (AI) becomes more prevalent in modern product development, it is essential to consider its impact on design for reliability (DFR). DFR has traditionally been a process that involves identifying potential failure modes, designing solutions, analyzing the design, testing the design, validating the design, and monitoring the product's performance throughout its lifecycle. AI has the potential to revolutionize each of these steps in the DFR process, providing new opportunities for improving product reliability and reducing costs.
What Is Artificial Intelligence? — Background
Artificial Intelligence (AI) is a rapidly growing field that has the potential to revolutionize how engineers design and develop products. AI refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, reasoning, and decision-making.
This chapter will provide a background on AI, its key components, and its potential applications for engineers. After that, we will move to specific applications in DFR.
AI has been around for many years, but it is only in recent years that it has gained widespread attention and investment. The field of AI is driven by advances in computing power, data storage, and data processing capabilities.
These advances have enabled the development of machine learning algorithms that can learn from data and improve their performance over time.
At its core, AI is made up of several key components. These include machine learning, natural language processing, computer vision, robotics, and expert systems. Each of these components plays a critical role in the development of AI systems.
Machine learning is the foundation of modern AI. It refers to the development of algorithms that can learn from data and improve their performance over time.
There are several different types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
Types of Machine Learning: Supervised
Supervised learning algorithms are used to train AI systems using labeled data. This means that the data has been labeled with the correct answer or output. For example, an AI system might be trained to recognize images of cats using a dataset of labeled cat images. The algorithm would then use this labeled data to learn how to recognize cats in new images.
Types of Machine Learning: UNSupervised
Unsupervised learning algorithms are used to train AI systems using unlabeled data. This means that the data has no predefined labels or categories. For example, an AI system might be trained to find patterns in a large dataset of financial transactions. The algorithm would then use this data to identify trends or anomalies that might be of interest to a financial analyst.
Types of Machine Learning: Reinforcement
Reinforcement learning algorithms are used to train AI systems using a reward-based system. The AI system is given a goal or objective, and it must learn how to achieve that goal by taking actions that lead to a reward.
For example, an AI system might be trained to play a game of chess. The algorithm would be given a reward for winning a game and a penalty for losing a game. Over time, the algorithm would learn which moves are more likely to lead to a win and which moves are more likely to lead to a loss.
Natural language processing (NLP) is another critical component of AI. It refers to the development of algorithms that can understand and interpret human language. NLP is used in a variety of applications, including chatbots, virtual assistants, and voice recognition systems. For example, an AI system might be used to develop a chatbot that can answer customer service questions. The algorithm would use NLP to understand the customer's question and provide an appropriate response. A recent example of NLP is ChatGPT.
Computer vision is another critical component of AI. It refers to the development of algorithms that can analyze and interpret images and video. Computer vision is used in a variety of applications, including facial recognition systems, object detection, and autonomous vehicles.
For example, an AI system might be used to develop an autonomous vehicle that can detect and avoid obstacles on the road. The algorithm would use computer vision to analyze images from cameras mounted on the vehicle and make decisions about how to navigate the road.
A real conceptual revolution took place when Computer Vision techniques were applied in car manufacturing processes and design to implement Artificial Intelligence with engineering predictive capabilities.
Robotics is another critical component of AI. It refers to the development of machines that can perform tasks autonomously or with minimal human intervention. Robotics is used in a variety of applications, including manufacturing, healthcare, and agriculture.
An example is the development of a robotic arm that can assemble products on a manufacturing line. The algorithm would use robotics to manipulate the arm and perform the assembly tasks.
Artificial Intelligence Applications
Let's explore some opportunities that AI offers for DFR applications.
Identifying Potential Failure Modes and AI
One area where AI has shown significant promise is in identifying potential failure modes. Machine learning algorithms can analyze vast amounts of data to identify patterns and anomalies that might indicate potential failure modes.
AI algorithms have been used to analyze flight data to identify potential safety issues before they become critical. By analyzing data from sensors and other sources, these algorithms can detect trends and patterns that might indicate a failure mode and alert engineers to the issue before it becomes a problem.
Solution Design and AI
Designing solutions is another area where AI can make a significant impact. Generative design, a technique that uses algorithms to create designs based on input criteria, has shown great promise in improving the design process. By inputting criteria such as weight, size, and material properties, generative design algorithms can quickly generate thousands of potential designs, each optimized for specific criteria. These designs can then be evaluated using simulation tools to determine their performance and reliability.
Validation and AI
Validation is another critical component of the process, and AI can play a significant role in improving validation efforts. Machine learning algorithms can analyze large amounts of data from testing and validation efforts, identifying trends and patterns that might indicate potential issues.
In the medical device industry, to quote an example, AI algorithms are being used to analyze patient data to identify potential safety issues. By analyzing data from clinical trials and post-market surveillance efforts, AI can detect trends and patterns that might indicate a safety issue and alert engineers to the problem.
Monitoring and AI
Monitoring is the final component of the Design for Reliability process, and AI can be used to improve the monitoring of products throughout their lifecycle. By analyzing data from sensors and other sources, AI algorithms can detect anomalies that might indicate a potential failure mode.
In the energy industry, AI algorithms are being used to monitor the performance of wind turbines. By analyzing data from sensors and other sources, AI algorithms can detect trends and patterns that might indicate a potential failure mode and alert engineers.
... and More
In addition to these specific applications, AI has the potential to revolutionize the DFR process by improving data analysis and decision-making. Machine learning algorithms can analyze vast amounts of data from various sources, providing engineers with insights and recommendations that might not have been possible before.
In the semiconductor industry, AI helps engineers to analyze data from wafer inspection tools to improve yield and reduce defects.
Design Analysis and AI in Design for Reliability
AI encompasses a range of technologies that enable machines to perform tasks that typically require human intelligence, such as perception, reasoning, and decision-making. One of the most promising areas of AI for DFR is machine learning (ML), which involves training algorithms to recognize patterns in data and make predictions based on those patterns. In particular, ML has been applied to analyze large volumes of data generated by complex systems.
The application of AI in DFR has the potential to revolutionize the product development process. By leveraging the power of ML algorithms, engineers can more accurately identify failure mechanisms and design products that are more reliable and robust. ML can also help to identify failure mechanisms that may not be apparent through traditional analysis techniques, enabling engineers to design more effective mitigation strategies.
Failure Mechanisms in Design for Reliability
The identification and mitigation of failure mechanisms are central to DFR. Failure mechanisms are the physical processes that result in product failures, such as fatigue, corrosion, wear, and fracture. Identifying failure mechanisms is critical for designing effective mitigation strategies that can prevent or delay failure.
There are a number of approaches that can be used to identify failure mechanisms, including experimental testing, simulation, and analysis of field data. Each approach has its own strengths and limitations, and engineers must select the most appropriate approach based on the specific requirements of the product and the available resources.
Experimental testing involves subjecting a product or component to a range of conditions to simulate real-world use. This approach can help to identify failure mechanisms and design effective mitigation strategies. However, it can be time-consuming and expensive, and it may not be possible to test all possible failure scenarios.
Simulation involves using computer models to predict the behavior of a product or component under different conditions. Simulation can be a powerful tool for identifying failure mechanisms and designing mitigation strategies. However, it requires accurate models of the product and the conditions in which it will be used, and it may not capture all aspects of the real-world environment.
Analysis of field failure data involves collecting data on product failures that have occurred in the field and analyzing that data to identify failure mechanisms. This approach can be useful for identifying failure mechanisms that may not have been anticipated during the design phase. However, it may be difficult to obtain sufficient data to draw meaningful conclusions, and the data may be subject to bias or other sources of error.
Machine Learning for Failure Mechanism Identification
Machine learning offers new opportunities for identifying failure mechanisms and designing effective mitigation strategies. ML algorithms can analyze large volumes of data generated by complex systems and identify patterns that may not be apparent through traditional analysis techniques. ML can also identify failure mechanisms that may not have been anticipated during the design phase, enabling engineers to design more effective mitigation strategies.
There are several types of ML algorithms that can be used for failure mechanism identification. They include supervised, unsupervised, and reinforcement learning.
Supervised learning involves training algorithms on labeled data, where the input data is labeled with the corresponding output. Unsupervised learning involves training on unlabeled data, where the algorithm must identify patterns on its own. Reinforcement learning involves training to take actions that maximize a reward signal.
In the context of failure analysis and mechanism identification, what are the different roles of the different learning approaches?
- Supervised learning can be used to identify patterns in experimental data or field data that are associated with specific failure mechanisms.
- Unsupervised learning can be used to identify patterns in data that may not be immediately apparent, and to group similar data together, potentially revealing previously unidentified failure mechanisms.
- Finally, reinforcement learning can be used to design effective mitigation strategies, by training algorithms to take actions that minimize the risk of failure.
One of the key benefits of ML for failure mechanism identification is that it can handle large amounts of data from a variety of sources, including sensor data, simulation data, and field data. ML algorithms can analyze this data to identify patterns and relationships that can be used to predict failure mechanisms and design effective mitigation strategies.
Another benefit of ML is that it can improve over time as more data becomes available. As more data is collected and analyzed, ML algorithms can be retrained to improve their accuracy and identify previously unidentified failure mechanisms. This can lead to a continuous improvement in life cycle costs, where the design of products and processes is continually refined to improve reliability.
Case Study: Machine Learning for Failure Mechanism Identification
One example of the application of ML for failure mechanism identification is in the aerospace industry. As we have seen in the Space Shuttle Challenger and in the Boeing 787 Dreamliner cases, aerospace systems are highly complex and operate in extreme environments, making them particularly susceptible to failure. Failure of critical components can have catastrophic consequences, making early prognostics a top priority for aerospace engineers and research centers .
To address this challenge, researchers at NASA have developed an ML-based approach for identifying failure mechanisms in aerospace systems. The approach involves training ML algorithms on large volumes of data from a variety of sources, including sensor data, simulation data, and field data.
The ML algorithms analyze the data to identify patterns and relationships that are indicative of specific failure mechanisms, such as fatigue or corrosion. The algorithms can then make predictions about the likelihood of specific failure mechanisms occurring in a given system, and recommend mitigation strategies to prevent or delay those failures.
This approach has been applied to a variety of aerospace systems, including aircraft engines, airframes, and avionics. By using ML to identify failure mechanisms and design effective mitigation strategies, engineers can improve the reliability and safety of aerospace systems, reducing the risk of catastrophic failure.
Summary and Conclusions
Design for reliability is a critical aspect of product development that aims to minimize the risk of failure and ensure the long-term performance of a product. One of the key challenges in DFR is identifying potential failure mechanisms and designing products to mitigate them.
AI, and in particular machine learning, offers new opportunities for identifying failure mechanisms and designing effective mitigation strategies. ML algorithms can analyze large volumes of data generated by complex systems and identify patterns that may not be apparent through traditional analysis techniques. ML can also identify failure mechanisms that may not have been anticipated during the design phase, enabling engineers to design more effective mitigation strategies.
Incorporating AI and machine learning into the DFR process can lead to more reliable and robust products, and reduce the risk of catastrophic failure. By leveraging the power of ML algorithms, engineers can more accurately identify failure mechanisms and design products that are better able to withstand the demands of real-world use.
Failure mechanisms are at the heart of Design for Reliability. By using AI to understand and mitigate those mechanisms, engineers will ensure the long-term performance and safety of their products.
 Reparability scores: helping consumers choose more sustainable products, News Announcement, 26 July 2022, Joint Research Centre (EU)
 for example, M.A. Chao, Ch. Kulkarni, K. Goebel and O. Fink Fusing physics-based and deep learning models for prognostics in "Reliability Engineering & System Safety", Volume 217, January 2022, 107961