用MATLAB进行预测维护

Day 1 of 2

Importing Data and Processing Data

Objective:将数据带入MATLAB并组织以进行分析，包括处理缺失值。通过提取和操纵数据的部分来处理原始的导入数据。

• Store data using MATLAB data types
• 用数据存储导入
• 处理数据缺少元素的数据
• Process big data with tall arrays

在数据中找到自然模式

Objective:Use unsupervised learning techniques to group observations based on a set of condition indicators and discover natural patterns in a data set.

• Find natural clusters within data
• Perform dimensionality reduction
• Evaluate and interpret clusters within data

Building Classification Models

Objective:Use supervised learning techniques to perform predictive modelling for classification problems. Evaluate the accuracy of a predictive model.

• 与分类学习者应用程序分类
• Train classification models from labeled data
• 验证训练有素的分类模型
• Improve performance with hyperparameter optimization

Day 2 of 2

Exploring and Analyzing Signals

Objective:Interactively explore and visualize signal processing features in data.

• Import, visualize, and browse signals to gain insights
• 对信号进行测量
• Compare multiple signals in the time and frequency domains
• 进行互动光谱分析
• Extract regions of interest
• Generate MATLAB scripts for automation

Preprocessing Signals to Improve Data Set Quality and Generate Features

Objective:Learn techniques to clean signal sets with operations such as resampling, removing outliers, and filling gaps. Interactively generate and rank features.

• Use resampling to handle nonuniformly sampled signals
• Fill gaps in uniformly sampled signals
• Perform resampling to ensure common time base across signals
• Use the Signal Analyzer app to design and apply filters
• Use File Ensemble Datastore to import data
• Use the Diagnostic Feature Designer app to automatically generate and rank features
• 使用信封光谱执行机械诊断
• Locate outliers and replace with acceptable samples
• Detect changepoints and perform automatic signal segmentation

Estimating Time to Failure

Objective:Explore data to identify features and train decision models to predict remaining useful life.

• Select condition indicators
• 使用寿命数据使用生存模型估算剩余的使用寿命
• Use run-to-threshold data to estimate remaining useful life using degradation models
• 使用跑到失败数据使用相似性模型估算剩余的使用寿命