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Reliability of NDT is affected by human factors, which have thus far received the least amount of attention in the reliability assessments. With increased use of automation, in terms of mechanised testing (automation-assisted inspection and the corresponding evaluation of data), higher reliability standards are believed to have been achieved. However, human inspectors, and thus human factors, still play an important role throughout this process and the risks involved in this application are unknown. The aim of this study was to explore for the first time the risks associated with mechanised NDT and find ways of mitigating their effects on the inspection performance. Hence, the objectives were to identify and Analyse potential risks in mechanised NDT and devise measures against them. To address those objectives, a risk assessment in form of a Failure Modes and Effects Analysis (FMEA) was conducted. This analysis revealed potential for failure during both the acquisition and evaluation of NDT data that could be assigned to human, technology, and organisation. Since the existing preventive measures were judged to be insufficient to defend the system from identified failures, new preventive measures were suggested.
Non-destructive testing (NDT) is a major contributor to the safe railway operation. Even though NDT reliability in railway maintenance is affected by human factors, there are only just a few studies published in this field so far. Education and training of the NDT personnel are some of the most important drivers of safe and reliable NDT. Continuously improving current practices and tools used for educational purposes can be achieved not only through technical con¬tent, but also through the attention to human factors. The aim of this study was to deepen the understanding of possible human-related risks in the manual ultrasonic inspection of the hollow railway axles and to suggest measures to improve the education and training of the NDT person¬nel. This was achieved by means of Failure Modes and Effects Analysis (FMEA) carried out with eight NDT experts and by a survey of 27 experienced inspectors. The results show that failures can happen throughout the entire NDT process. Prevention of those failures could be improved through the optimization of the organization, technology, documentation and regulations, working conditions and the general process, and through the optimization of the formal education and training. Specialized training of the executives, extended training of the supervisors and the inspectors and improvement of the inspection documentation have been suggested. The study also showed potential for the improvements of the inspection in the field.
Non-destructive testing (NDT) is a major contributor to the safe railway operation. Even though NDT reliability in railway maintenance is affected by human factors, there are only just a few studies published in this field so far. Education and training of the NDT personnel are some of the most important drivers of safe and reliable NDT. Continuously improving current practices and tools used for educational purposes can be achieved not only through technical content, but also through the attention to human factors. The aim of this study was to deepen the understanding of possible human-related risks in the manual ultrasonic inspection of the hollow railway axles and to suggest measures to improve the education and training of the NDT personnel. This was achieved by means of Failure Modes and Effects Analysis (FMEA) carried out with eight NDT experts and by a survey of 27 experienced inspectors. The results show that failures can happen throughout the entire NDT process. Prevention of those failures could be improved through the optimization of the organization, technology, documentation and regulations, working conditions and the general process, and through the optimization of the formal education and training. Specialized training of the executives, extended training of the supervisors and the inspectors and improvement of the inspection documentation have been suggested. The study also showed potential for the improvements of the inspection in the field.
This presentation offers a holistic view on the assessment of reliability of NDT including the intrinsic reliability (typically expressed in terms of probability of detection (POD) curves), application, human and organisational factors. In addition to POD, advanced methods such as multiparameter POD, volume POD and POD for combined data are presented. Human and organisational factors in NDT require a systematic approach, i.e. it is not just the individual that determines how the inspections are carried out but also the interactions of individuals with the technology, team, organisation and the extra-organisational environment. Lessons learned from the literature as well as from own studies are presented.
Оперативная характеристика обнаружения как метод оценки достоверности неразрушающего контроля
(2021)
Оперативные характеристики обнаружения в течение многих лет успешно применяют-ся при оценке достоверности неразрушающего контроля. Основными проблемами для их использования, как и при применении любого из методов оценки достоверности, яв-ляются отсутствие информации о действительной дефектной ситуации и ограниченное количество дефектов в образцах объектов контроля. Дополнительной проблемой постро-ения оперативной характеристики обнаружения является отсутствие информации о раз-мерах дефектов и отсутствие универсального размера для различных типов дефектов.
В статье обобщён опыт построения оперативных характеристик по результатам ультра-звукового контроля.
That human factors (HF) affect the reliability of NDT is not novelty. Still, when it comes to reliability assessments, the role of people is often neglected. Reliability is typically expressed in terms of POD curves, and the effects of human and organisational factors on the inspection are typically tackled by the regulations, procedures and by the qualification and training of the inspection personnel. However, studies have shown that even the most experienced personnel can make mistakes and that the reliability in the field is never as high as the reliability measured in the POD experiments. Generally, HF are considered too unpredictable and too uncontrollable to model. If that is the fact, then what can we do? The engineering perspective to this problem has often been to find ways to automate inspections and, recently, to make use of artificial intelligence tools to decrease the direct effect of people on the inspection results and improve the overall efficiency and reliability. However, despite automation and AI, people remain the key players, though their tasks change. The contemporary approach to HF is not to engineer them out of the system but to design human-machine systems that make the best use of both. In this talk, ways of tackling HF in the design of systems and processes will be presented.