RUL数据集扩展——CMAPSS

微软云平台Azure tutorial based on CMAPSS using LSTM

code

1. Import content

上面的GitHub是由微软云平台Azure关于Deep Learning for Predictive Maintenance整理的内容，包括build an LSTM network for the data set and scenario described at Predictive Maintenance Template to predict remaining useful life of aircraft engines. In summary, the template uses simulated aircraft sensor values to predict when an aircraft engine will fail in the future so that maintenance can be planned in advance.
Since predictive maintenance is not a typical domain for deep learning, its application is an open area of research.可以看出RUL是具有实际应用价值的

2. Coding Idea

• Requirements:
This notebook uses keras deep learning library with Microsoft Cognitive Toolkit CNTK as backend.

• Step Sequence
Step1: Data ingestion
In the following section, we ingest the training, test and ground truth datasets from azure storage. The training data consists of multiple multivariate time series with “cycle” as the time unit, together with 21 sensor readings for each cycle. Each time series can be assumed as being generated from a different engine of the same type. The testing data has the same data schema as the training data. The only difference is that the data does not indicate when the failure occurs. Finally, the ground truth data provides the number of remaining working cycles for the engines in the testing data. You can find more details about the type of data used for this notebook at Predictive Maintenance Template.
Step2: Data Preprocessing
First step is to generate labels for the training data which are Remaining Useful Life (RUL), label1 and label2 as was done in the Predictive Maintenance Template. Here, we will only make use of “label1” for binary clasification, while trying to answer the question: is a specific engine going to fail within w1 cycles?
Step3: Modeling

3. Related Reading

• Predictive Maintenance Template https://gallery.azure.ai/Collection/Predictive-Maintenance-Template-1
Summary
This experiment demonstrates the steps in building a predictive maintenance solution.
This predictive maintenance template focuses on the techniques used to predict when an in-service machine will fail, so that maintenance can be planned in advance. In particular, this template illustrates the process of predicting future failure events in the scenario of aircraft engine failures [1].
Three modeling solutions are provided in this template to accomplish the following tasks. – Regression: Predict the Remaining Useful Life (RUL), or Time to Failure (TTF). – Binary classification: Predict if an asset will fail within certain time frame (e.g. days). – Multi-class classification: Predict if an asset will fail in different time windows: E.g., fails in window [1, w0] days; fails in the window [w0+1,w1] days; not fail within w1 days

• Predictive Maintenance Template https://gallery.azure.ai/Collection/Predictive-Maintenance-Template-2
Same as 1
• Predictive Maintenance Template https://gallery.azure.ai/Collection/Predictive-Maintenance-Template-3
This predictive maintenance template focuses on the techniques used to predict when an in-service machine will fail, so that maintenance can be planned in advance. The template includes a collection of pre-configured machine learning modules, to enable an end-to-end solution from data processing to deploying of the machine learning model.
This template uses the example of simulated aircraft engine run-to-failure events to demonstrate the predictive maintenance modeling process. The implicit assumption of modeling data as done below is that the asset of interest has a progressing degradation pattern, which is reflected in the asset’s sensor measurements. By examining the asset’s sensor values over time, the machine learning algorithm can learn the relationship between the sensor values and changes in sensor values to the historical failures in order to predict failures in the future.

4. 扩展：

• Azure是Microsoft開發的雲端平台，整合分析、IoT、運算、AI、資料庫、行動、網路、儲存體及Web等多項雲服務，彈性且支援多項程式語言、工具或架構，方便您進行建置、部署及管理應用程式，介面簡潔、人性化，方便使用者輕鬆上手。 … 課程中將引導您於Microsoft Azure雲端平台上進行AI體驗操作。

• 目前，Keras 有三个后端实现可用: TensorFlow 后端，Theano 后端，CNTK 后端。
• TensorFlow 是由 Google 开发的一个开源符号级张量操作框架。
• Theano 是由蒙特利尔大学的 LISA Lab 开发的一个开源符号级张量操作框架。
• CNTK 是由微软开发的一个深度学习开源工具包。