failure density function

简明释义

故障频率函数

英英释义

例句

1.By analyzing the failure density function 失效密度函数, we can identify critical points in the manufacturing process.

通过分析失效密度函数 failure density function，我们可以识别制造过程中的关键点。

2.In reliability engineering, the failure density function 失效密度函数 helps in assessing product lifespan.

在可靠性工程中，失效密度函数 failure density function 有助于评估产品的使用寿命。

3.In a statistical model, the failure density function 失效密度函数 provides insights into failure rates.

在统计模型中，失效密度函数 failure density function 提供了关于失效率的见解。

4.The engineer used the failure density function 失效密度函数 to predict the likelihood of component failures over time.

工程师使用了失效密度函数 failure density function 来预测组件在一段时间内失效的可能性。

5.The failure density function 失效密度函数 is essential for developing maintenance schedules for machinery.

对于制定机械维护计划来说，失效密度函数 failure density function 是必不可少的。

作文

In the field of reliability engineering and statistics, understanding various functions that describe the behavior of systems is crucial. One such important concept is the failure density function, which plays a significant role in assessing the reliability of products and systems. The failure density function (中文释义: 失效密度函数) provides a mathematical representation of the likelihood of failure occurring at any given time during the lifespan of a product. This function is particularly useful for engineers and researchers who aim to predict when failures are likely to happen, allowing them to implement preventive measures and enhance the overall reliability of their designs.The failure density function is derived from the more general concept of the probability density function (PDF). In essence, while the PDF describes the distribution of a continuous random variable, the failure density function specifically focuses on the time until failure occurs. This makes it an invaluable tool in industries where equipment reliability is critical, such as aerospace, automotive, and manufacturing.To better understand how the failure density function works, let’s consider an example. Imagine a manufacturer producing light bulbs. Each light bulb has a certain expected lifespan, but not all bulbs will fail at the same time. By analyzing historical data, the manufacturer can construct a failure density function that illustrates the probability of a bulb failing at various points in time. This function might show that there is a high probability of failure shortly after the bulb is turned on, indicating a 'burn-in' period, followed by a relatively stable phase where failures occur less frequently, and finally an increase in failure rates as the bulbs approach the end of their lifespan. By utilizing the failure density function, manufacturers can make informed decisions regarding warranty periods, replacement schedules, and maintenance practices. For instance, if the function indicates that a significant number of bulbs tend to fail after 1,000 hours of use, the manufacturer might choose to offer a warranty that covers this critical period, thus enhancing customer satisfaction and reducing the likelihood of product returns.Moreover, the failure density function is also essential in the context of system-level analysis. In complex systems where multiple components interact, understanding the individual failure density functions of each component allows engineers to assess the overall reliability of the system. This is often done through techniques such as fault tree analysis or reliability block diagrams, which help visualize how failures propagate through a system and affect its performance.In conclusion, the failure density function (中文释义: 失效密度函数) is a fundamental concept in reliability engineering that aids in predicting and analyzing failures over time. Its applications are widespread across various industries, providing valuable insights that lead to improved product design, enhanced safety measures, and increased customer satisfaction. As technology continues to advance and systems become more complex, the importance of accurately understanding and applying the failure density function will only grow, making it an essential topic for engineers and researchers alike.