mean time before failure

简明释义

故障前平均时间修理间隔平均时间故障前平均时间

英英释义

例句

1.Manufacturers often advertise their products' mean time before failure to attract customers looking for reliability.

制造商通常宣传其产品的故障平均时间以吸引寻求可靠性的客户。

2.During the testing phase, we recorded a mean time before failure of 750 hours for the prototype.

在测试阶段，我们记录到原型的故障平均时间为750小时。

3.To improve reliability, the engineers focused on increasing the mean time before failure in their latest design.

为了提高可靠性，工程师们专注于在最新设计中增加故障平均时间。

4.The new machine has a mean time before failure of 500 hours, which means it is expected to run without issues for that duration.

这台新机器的故障平均时间为500小时，这意味着它预计在此期间不会出现问题。

5.A high mean time before failure indicates a robust system that is less likely to fail unexpectedly.

高故障平均时间表示系统稳健，不太可能意外故障。

作文

In the realm of engineering and manufacturing, reliability is a crucial aspect that can significantly impact productivity and safety. One of the key metrics used to assess the reliability of a system or component is the mean time before failure. This term refers to the average time that elapses before a system experiences a failure, which can be critical in planning maintenance schedules and ensuring operational efficiency. Understanding the mean time before failure is essential for engineers, managers, and technicians alike, as it provides insights into how often failures might occur and helps in making informed decisions about design, production, and maintenance processes.To grasp the significance of the mean time before failure, we must first consider its implications in various industries. For instance, in the aerospace sector, where safety is paramount, knowing the mean time before failure of aircraft components can guide manufacturers in implementing rigorous testing and maintenance protocols. A higher mean time before failure indicates greater reliability, which is crucial for preventing accidents and ensuring passenger safety. Similarly, in the automotive industry, manufacturers strive to enhance the mean time before failure of their vehicles to improve customer satisfaction and reduce warranty claims.The calculation of the mean time before failure typically involves analyzing historical data on failures and operational hours of a particular component or system. By gathering data over time, engineers can determine the average time between failures, allowing them to identify trends and make predictions about future performance. This information can also assist in identifying potential weaknesses in design or materials, prompting improvements that enhance overall reliability.Moreover, the mean time before failure is not just a static figure; it can change over time due to various factors such as wear and tear, environmental conditions, and advancements in technology. For example, as new materials and manufacturing techniques are developed, the mean time before failure for certain components may increase, reflecting improved durability and performance. Therefore, continuous monitoring and analysis are essential to maintain an accurate understanding of a system's reliability.In addition to its technical implications, the mean time before failure also carries significant economic weight. Companies that effectively manage their mean time before failure can reduce downtime, lower maintenance costs, and enhance productivity. By investing in preventive maintenance and quality assurance practices, organizations can extend the lifespan of their equipment and minimize the likelihood of unexpected failures. This proactive approach not only saves money but also fosters a culture of reliability and trust within the organization.In conclusion, the mean time before failure is a vital metric in assessing the reliability of systems and components across various industries. Its importance cannot be overstated, as it influences safety, operational efficiency, and economic viability. By understanding and effectively managing the mean time before failure, organizations can enhance their products and services, ultimately leading to greater customer satisfaction and success in the marketplace. As technology continues to evolve, the pursuit of improved mean time before failure will remain a key focus for engineers and manufacturers alike, driving innovation and excellence in their respective fields.

在工程和制造领域，可靠性是一个至关重要的方面，它可以显著影响生产力和安全性。用于评估系统或组件可靠性的关键指标之一是故障前平均时间。这个术语指的是在系统发生故障之前经过的平均时间，这在规划维护时间表和确保操作效率时至关重要。理解故障前平均时间对工程师、管理者和技术人员来说都是必不可少的，因为它提供了有关故障可能发生频率的见解，并有助于做出关于设计、生产和维护过程的明智决策。要理解故障前平均时间的重要性，我们必须首先考虑其在各个行业中的影响。例如，在航空航天行业，安全至关重要，了解飞机组件的故障前平均时间可以指导制造商实施严格的测试和维护协议。较高的故障前平均时间表明更大的可靠性，这对于防止事故和确保乘客安全至关重要。同样，在汽车行业，制造商努力提高其车辆的故障前平均时间以改善客户满意度并减少保修索赔。故障前平均时间的计算通常涉及分析特定组件或系统的故障和运行小时的历史数据。通过收集数据，工程师可以确定故障之间的平均时间，从而识别趋势并预测未来的性能。这些信息还可以帮助识别设计或材料中的潜在弱点，促使改进以增强整体可靠性。此外，故障前平均时间不仅是一个静态数字；它可能会随着时间的推移而变化，受到磨损、环境条件和技术进步等各种因素的影响。例如，随着新材料和制造技术的发展，某些组件的故障前平均时间可能会增加，反映出更好的耐久性和性能。因此，持续监测和分析对于保持对系统可靠性的准确理解至关重要。除了其技术意义外，故障前平均时间还具有重要的经济意义。有效管理故障前平均时间的公司可以减少停机时间，降低维护成本，并提高生产力。通过投资于预防性维护和质量保证实践，组织可以延长设备的使用寿命，并最小化意外故障的可能性。这种主动的方法不仅节省了资金，而且在组织内部培养了可靠性和信任的文化。总之，故障前平均时间是评估各个行业系统和组件可靠性的一个重要指标。它的重要性不容小觑，因为它影响着安全性、操作效率和经济可行性。通过理解和有效管理故障前平均时间，组织可以提升其产品和服务，最终实现更高的客户满意度和市场成功。随着技术的不断发展，追求改进的故障前平均时间将继续成为工程师和制造商的重点，推动各自领域的创新和卓越。