TIMEOUT FOR TECH, RAILWAY AGE SEPTEMBER 2022 Issue: Fitness-for-service assessments of an engineering system are designed to address a fundamental question: Is the system safe to use for its intended purpose at a given time?
Welcome to “Timeout for Tech.” Every month, we study a technical topic that rail industry professionals have been asking to learn more about. This month, we focus on fitness-for-service assessments of engineered systems.
Many fitness-for-service procedures are somewhat similar to a routine physical examination of the person by a doctor. In fact, much of the underlying math is essentially identical when evaluating an engineering system for safe use or a person’s risk factors for serious illness. Figure 1 is presented in response to the idea that an assessment of the safe use of an engineering system has comparisons to assess a person’s health. Let’s get started.
In a routine physical exam, several tests are performed, and the results are compared against predetermined ranges of values considered “healthy”. Tests performed during testing, and the value ranges for each test, are usually determined statistically to reduce the likelihood that a significant risk factor for serious illness will not be detected. Note, however, that this probability can never be identically zero – theoretically or practically. The predefined “healthy” value ranges for each test, and the probabilities of detection of target risk factors, are the results of comprehensive studies and expert discussion and decision-making by experts. The results can all be “healthy”, and I wish that to all my readers! Some results may indicate a concern that needs attention. Some results may appear indicating a serious health problem that needs immediate intervention. Now what about the integrity of the engineering system?
Fitness-for-service assessments of an engineering system are designed to address a fundamental question: Is the system reasonably safe to use for its intended purpose at a given time? Continuing our analogy, we ask, “Is the system sound?” To answer these questions, we examine the system and the condition of its components and compare our observations and measurements against pre-set tolerances. The measurements taken and the predetermined tolerances for each measurement are usually determined statistically to reduce the possibility of system component failure before the next evaluation cycle begins. Note, however, that this probability can never be identically zero – theoretically or practically. Pre-determined measurement tolerances and target probabilities of failure are the results of comprehensive studies, expert discussions and expert decision-making. At the end of the process, we determine the integrity of the system for its intended purpose, evacuate the system for normal operation, flag it for reduced operation and further evaluation, or turn it off for repair or replacement.
With this introduction, and continuing with the analogy, let’s take a look at the process of checking the performance of an engineering system. There is an area of engineering consultancy, technology development, and research called Structural Health Monitoring, or SHM. A common application of SHM involves deploying a strategic inventory of electronic sensors on an engineering system along with data processing algorithms, which often include artificial intelligence (AI) methods. Most often, SHM is optimized to measure the dynamic performance of a system in use, alerting when unusual or extreme events occur that could lead to system failure. Some experts refer to SHM as giving the engineering system the ability to record “pain”. One of my students once described SHM as “learning a silent child to cry” so that his parents can better respond to his needs. Anyone who has bought a car in recent years has noticed something similar to these concepts.
Over the past 20 years, our cars have become complete with dozens of electronic sensors, embedded computers, software systems and data visualization systems. For example, as shown in Figure 2, indicator lights sometimes appear on my dashboard which is simple and intuitive. With some of these indicators, like a “check engine” or an ominous red exclamation point warning that my brake system is somehow under the weather, I’ll make an appointment with a repair shop.
When I leave my car at the shop, the first thing the mechanic will do is connect a computer to the on-board diagnostic port or OBD port. Although it comes from the same data sources that activate my simple indicator light, the data provided to the mechanic will be much more detailed and tailored to help the mechanic solve problems with my vehicle. Essentially, the vehicle is “actively” involved in assessing its suitability for service – like a patient’s answer to a doctor’s questions.
There are quite a few applications for “smart sensing” systems in the rail industry that provide data streams that aid in fitness-for-service assessments. And more is under development every year.
For example, we have on the side of the path:
- Engineering path measurement systems.
- Railroad side gauge systems.
- Railway defect detection systems.
- Ultrasound systems (for subsurface cracks).
- Electromagnetic field imaging systems (for surface cracks and striations).
- Tie checking systems.
- Ballast inspection systems.
- optical systems.
- Light detection and ranging (LIDAR).
- Ground penetrating radar (GPR).
There are also several road systems available to monitor rolling stock traffic:
- Truck performance detectors.
- Acoustic bearing detectors.
- Heat detectors.
- Thermal wheel detectors.
- Wheel impact load detectors.
- Wheel alignment systems.
- Automatic crack/broken wheel detectors.
- Ultrasound systems (for subsurface fatigue cracks).
- Electromagnetic field imaging systems (for surface cracks and striations).
- Optical systems (for partially damaged rims).
Certainly, cutting-edge technology helps us make more accurate and accurate assessments of the state of the system in real time – especially when it is in use and under overburden. But there will always be fundamental questions regarding setting threshold limits for system performance and permissible degradation. This brings us to the most difficult and uncomfortable part of every fitness-to-service discussion: setting a goal for an acceptable probability of failure. It is most sensitive in scenarios where human life is in danger. So I ask the question: What is the acceptable probability of failure when lives are in danger? Many of us tend to say, “Zero! Failure is not a choice!”
The failure of fundamentally zero engineering systems is not possible, and certainly not today. Our imperfect scientific and engineering knowledge, imperfect manufacturing capabilities, almost complete inability to control Mother Nature, imperfect decision-making models, the reality of human error, and other inherent flaws combine in complex ways and prevent the complete elimination of the risk of failure.
To mitigate the most serious consequences of failure, we have nothing above available Our best possible. We must commit them to all tasks at all times, accurately model what this means in terms of the risk of failure, and report the outcome. good well as ours Acceptable probability of failure. We can’t do better than that, and no one deserves less when the safety of life is at stake.
Dr. Fry is Vice President of Fry Technical Services, Inc. (https://www.frytechservices.com/). He has 30 years of experience researching and consulting on fatigue and fracture behavior of structural and welding metals. His research findings have been incorporated into international codes of practice used in the design of structural components and systems, including structural welds, railway bridges, highways, and high-rise commercial buildings in seismic risk areas. He has extensive experience in conducting on-site testing of rail bridges under direct train loading, including high-speed passenger trains and heavy-duty freight trains. His research, publications, and advisory advances state-of-the-art technology in the monitoring of structural health and the detection of skeletal anomalies.
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