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首页> 外文会议>2009 International Conference on Signal Processing Systems >Time Series Behaviour of Lower Arm Suspension Fatigue Data Using Classical Decomposition Method
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Time Series Behaviour of Lower Arm Suspension Fatigue Data Using Classical Decomposition Method

机译：下臂悬架疲劳数据的时间序列行为的经典分解方法

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摘要

The study of time series behaviour refers to the analysis of certain unique attributes that exist in the time series data. The presence of these attributes in the data series may influence the decision making process. These attributes are generally grouped into four main component types which are trend, cyclical, seasonal and irregular components. In this study, fatigue signal data with three different road factors from a lower arm suspension for a mid-sized car were used as the case study. ldquoClassical decompositionrdquo time series method was used to segregate and to analyse the existence components in a systematic manner. Although fatigue data is a time series signal, not all components were considered. This is due to the nature of fatigue behaviour itself which is different from a normal time series data. From the study, it was found that only trend, cyclical and irregular component existed in the fatigue data signal. The study also revealed the additive effect that existed between these three types components as the absolute sizes of the seasonal variation are independent of each other.
机译：对时间序列行为的研究是指对时间序列数据中存在的某些独特属性进行分析。这些属性在数据系列中的存在可能会影响决策过程。这些属性通常分为四种主要成分类型，即趋势成分，周期性成分，季节性成分和不规则成分。在本研究中，以中型汽车下臂悬架的三种不同道路因素的疲劳信号数据为例。使用“经典分解”时间序列方法对系统中存在的成分进行分离和分析。尽管疲劳数据是时间序列信号，但并未考虑所有分量。这是由于疲劳行为本身的性质与正常时间序列数据不同。从研究中发现，疲劳数据信号中仅存在趋势，周期性和不规则成分。研究还揭示了这三种类型成分之间存在的累加效应，因为季节性变化的绝对大小彼此独立。

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