使用 Cortana Intelligence Suite

建构预测性维护解决方案

Mark Chen陈景忠Advanced Analytics & Artificial Intelligence

Technology Specialist

Intelligent Cloud Global Black Belt

Greater China Region

课程代码 AI302

Microsoft Tech Summit 2017

微软技术暨生态大会

1 2 3

✓ Predictive

Maintenance

Overview

✓ Cortana

Intelligence

Suite

✓ Predictive

Maintenance

Process

Agenda

3

4

✓ Predictive

Maintenance

– Case studies

Predictive Maintenance Overview

Microsoft Tech Summit 2017

微软技术暨生态大会

预测性维护| 优势

发现连接到各种维修问题的模式

减少运营风险

控制维护成本

提高整体品牌形象

降低库存成本减少不必要且费时的维修作业

IoT 应用中的重要任务 | 目标: 提高生产/维修的效率

传统维修计划

故障的检测、诊断与预测

模型驱动

Parts 层级

非常有限的时间

确保机器运行的可靠性

CIS 的预测性维护

Goal

Scope

Approach

Data

Tasks故障的检测、诊断与预测以及维修操作建议提高生产维修的效率

数据驱动

系统层级, 组件层级

数据流（时变特征）、多个数据源

提高生产 and/or 维护的效率

好的原料供应，更好的

产品

预测性维护 | 如何开始?

明确的问题

量测数据的方法

准确的数据

数据的联机

大量的数据

?

10010

01010

| 数据源

故障事件的历史记录

机器设备的操作条件

维修的历史记录

机器设备的特征环境特征

机器设备相关的信息

Merging Data Sources

| 特征工程

Potential features of Feature Engineering

Change from initial value

Velocity of change

Frequency count over a predefined threshold

Rolling aggregates Tumbling aggregates

1 2

| 建立模型

BINARY

CLASSIFICATIONREGRESSION

MULTICLASS

CLASSIFICATION

ANOMALY

DETECTION

10010

01010

• 1. How much/how many? Regression

• 2. Which category? Classification

• 3. Which groups? Clustering

• 4. Is it weird? Anomaly detection

• 5. Which option? Recommendation

| 模型训练 & 验证方法

交叉验证

模型效能测试

数据依时间拆分训练数据、测试数据

处理不平衡数据

Cortana

Intelligence

Suite

Microsoft Tech Summit 2017

微软技术暨生态大会

Cortana Intelligence Suite

Business apps

Custom apps

Sensors and devices

People

Automated systems

Data Intelligence

Cortana Intelligence Platform & Solution

Action

Apps

Cortana Intelligence Platform

Intelligence

Dashboards &

Visualizations

Information

Management

Big Data Stores Machine Learning

and Analytics

CortanaEvent HubsHDInsight

(Hadoop and

Spark)

Stream

Analytics

Data Intelligence Action

People

Automated Systems

Apps

Web

Mobile

Bots

Bot

FrameworkSQL Data

WarehouseData Catalog

Data Lake

Analytics

Data Factory Machine

LearningData Lake Store

Cognitive

Services

Power BI

Data

Sources

Apps

Sensors

and

devices

Data

Cortana Intelligence Solution

Solutions by industry Solutions by department

Discrete

manufacturing

Connected

field service

Asset

management

Connected

product

innovation

Banking and

capital markets

Customer

insight

Fraud

management

Risk

management

Next best

action

Retail and

consumer

goods

Retail

execution

Inventory

optimization

Personalization

Government

Citizen services

Connected

field services

Case

management

Traffic

optimization

Healthcare

Patient care

coordination

Operational

analytics

Clinical

analytics

Marketing

Churn analytics

Customer

feedback

analytics

Marketing

optimization

Personalization

Product

innovation

Product

recommendatio

n

Sales

Cross-sell and

upsell

Customer

acquisition

Lead generation

and opportunity

scoring

Service

Contact center

analytics

Service analytics

Finance

Financial

analytics

Financial

forecasting

Fraud

management

Risk

management

Operations

Demand

forecasting

Inventory

optimization

IT Anomaly

Insights

Operational

efficiency

Procurement

insight

Spend insight

Supplier insight

Workforce

Employee

insight

HR insight

Pay for

performance

https://www.microsoft.com/en-us/cloud-platform/cortana-intelligence-suite-industry-solutions

https://www.microsoft.com/en-us/cloud-platform/cortana-intelligence-suite-role-solutions

Predictive Maintenance for Aerospace

Summary

This Predictive Maintenance solution

monitors aircraft and predicts the remaining

useful life of aircraft engine components.

https://gallery.cortanaintelligence.com/Solution/Predictive-Maintenance-for-Aerospace-4

Predictive

Maintenance

Process

Microsoft Tech Summit 2017

微软技术暨生态大会

1 2 3 4

✓ Planning,

Environment,

Ingest

✓ Explore and

Visualize

✓ Generate

Features

✓ Create

Models

Predictive Maintenance

5

✓ Train

Models

6

✓ Consume

Business Understanding

Data Understanding

Data Preparation Modeling Evaluating Deploying

1

✓ Planning,

Environment,

Ingest

19

Business Understanding

Compose and orchestrate data services at scale

INGEST

SQL

<>

SQL

DATA SOURCES

{ }

SQL

• Create, schedule, orchestrate, and manage data pipelines

• Visualize data lineage

• Connect to on-premises and cloud data sources

• Monitor data pipeline health

• Automate cloud resource management

• Move relational data for Hadoop processing

• Transform with Hive, Pig, or custom code

Information

Management

Event Hubs

Data Catalog

Data Factory

Get more value from your enterprise data assets

Information

Management

Event Hubs

Data Catalog

Data Factory

• Spend less time looking for data, and more time getting value from it

• Register enterprise data sources, discover data assets and unlock their potential, and capture tribal knowledge to make data understandable

• Bridge the gap between IT and the business, allowing everyone to contribute their insights, tags, and descriptions

• Intuitive search and filtering to understand the data sources and their purpose

• Let your data live where you want; connect using tools you choose

• Integrate into existing tools and processes with open REST APIs

Information Management & Big Data Stores

Big Data Stores

SQL Data

Warehouse

Data Lake Store

Data

Sources

Apps

Sensors

and devices

Data

Information

Management

Event Hubs

Data Catalog

Data Factory

A hyper-scale repository for big data analytics workloads

• A Hadoop Distributed File System for the cloud

• No fixed limits on file size

• No fixed limits on account size

• Unstructured and structured data in their native format

• Massive throughput to increase analytic performance

• High durability, availability, and reliability

• Azure Active Directory access control

LOB

Applications

SocialDevices

Clickstream

Sensors

Video

Web

Relational

HDInsight

ADL Analytics

Machine Learning

Spark

R

ADL Store

Big Data Stores

SQL Data

Warehouse

Data Lake Store

Elastic data warehouse as a service with enterprise-class features

• Petabyte scale with massively parallel processing

• Independent scaling of compute and storage—in seconds

• Transact-SQL queries across relational and non-relational data

• Full enterprise-class SQL Server experience

• Works seamlessly with Power BI, Machine Learning, HDInsight, and Data Factory

Power BI

App ServiceSQL Database

SQL Data Warehouse

Machine Learning

Hadoop

Intelligent App

Big Data Stores

SQL Data

Warehouse

Data Lake Store

2

✓ Explore and

Visualize

25

Data Understanding

25

Data VisualizationCold Path - predictedHot Path - recorded and tracked

预测性维护

3

✓ Generate

Features

Data Preparation

The Mean Time to Failure (MTF) Feature Engineering

Rolling Aggregates

Tumbling aggregates

Static Features

The final feature table after feature engineering steps

discussed in the earlier section should resemble the

following example data schema when time unit is a

day:

Asset ID Time Feature Columns Label

1 Day 1

1 Day 2

... ...

2 Day 1

2 Day 2

... ...

Modeling & Evaluating

✓ Create Models

✓ Train Models

4

✓ Create Models

✓ Train Models

27

Modeling

5

Evaluating Modelling Techniques

Binary Classification

Regression

Multi-Class Classification

Multi-Class Classification for root cause prediction

Training, Validation and Testing Methods

Easily build, deploy, and share predictive analytics solutions

• Simple, scalable, cutting edge. A fully managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions.

• Deploy in minutes. Azure Machine Learning means business. You can deploy your model into production as a web service that can be called from any device, anywhere and that can use any data so urce.

• Publish, share, monetize. Share your solution with the world in the Gallery or on the Azure Marketplace.

Machine Learning

and Analytics

HDInsight

(Hadoop and

Spark)

Stream

Analytics

Data Lake

Analytics

Machine

Learning

https://studio.azureml.net/

Which category(Classification)

How much/many(Regression)

Is it odd (Anomaly)

Which group(Clustering,

Recommender)

Machine Learning AlgorithmsSplit into two main categories:

• Supervised learning

• Predicting the future

• Learn from known past examples to predict future

• Labels provided

• Unsupervised learning

• Making sense of data

• Understanding the past

• Learning the structure of data

• Labels no provided

Big data analytics made easy

• Analyze data of any kind and size

• Develop faster, debug and optimize smarter

• Interactively explore patterns in your data

• No learning curve—use U-SQL, Spark, Hive, HBase and Storm

• Managed and supported with an enterprise-grade SLA

• Dynamically scales to match your business priorities

• Enterprise-grade security with Azure Active Directory

• Built on YARN, designed for the cloud

Data Lake Analytics

SQL DW SQL DB Storage BlobsData Lake Store SQL DB in a VM

Machine Learning

and Analytics

HDInsight

(Hadoop and

Spark)

Stream

Analytics

Data Lake

Analytics

Machine

Learning

Comprehensive set of managed Apache big data projects

• Scale to petabytes on demand

• Process unstructured and semi-structured data

• Develop in Java, .NET, and more

• Skip buying and maintaining hardware

• Deploy in Windows or Linux

• Spin up an Apache Hadoop cluster in minutes

• Visualize your Hadoop data in Excel

• Easily integrate on-premises Hadoop clusters

Core Engine

Batch

Map Reduce

Script

Pig

SQL

Hive

NoSQL

HBase

Streaming

Storm

In-Memory

Spark

Machine Learning

and Analytics

HDInsight

(Hadoop and

Spark)

Stream

Analytics

Data Lake

Analytics

Machine

Learning

6

✓ Consume

32

Deploying

Build applications that understand people

• Faces, images, emotion recognition and video intelligence

• Spoken language processing, speaker recognition, custom speech recognition

• Natural language processing, sentiment and topics analysis, spelling errors

• Complex tasks processing, knowledge exploration, intelligent recommendations

• Bing engine capabilities for Web, Autosuggest, Image, Video and News

Intelligence

Cortana

Bot

Framework

Cognitive

Services

Your bots – wherever your users converse

Intelligence

Cortana

Bot

Framework

Cognitive

Services

• Bot Connector Service: A service to register your bot, configure channels and publish to the Bot Directory. Connect your bot(s) seamlessly to text/sms, Office 365 mail, Skype, Slack, Twitter, and more.

• Bot Builder SDK: An open source SDK hosted on GitHub. Everything you need to build great dialogs within your Node.js or C# bot

• Bot Directory: A public directory of bots registered through the Bot Connector Service. Discover, try, and add bots to conversation experiences

6

✓ Consume

35

Deploying

Predictive

Maintenance –

Case studies

Microsoft Tech Summit 2017

微软技术暨生态大会

37

提升机队使用率

• 降低落地时间增加6%飞机机队利用率

• 提升年收入达 4.5 亿美金

降低营运及资产投资成本

• 每年每架飞机节省1%油耗成本达25万美金

• 预测性维护协助拥有500台飞之机航空公司每年省下1亿美金之资产投资

创造高获利服务商业模式

智能监控系统

| 架构

Data Schema Definitions, Interoperability & Technical Architecture

Skills, Certifications, Learning (LMS) & Human Capital Management (HCM)

Man

ufa

ctu

rin

g

Desi

gn

& E

ng

ineeri

ng

Operational Performance Monitoring and service / Product Feedback

mBOM & MES Manufacturing instructions sBOM & Manuals, Tasks, Forms & Records

ContractsRegulatory Forms & Certifications Content Lifecycle Management (CLM)

S6000T

S9000D

S1000D

CAE

CAE

MES

CAM

PLM

MRP II

ProvisioningProcurement &

Distribution

Autonomic LogisticsS2000M

Regulatory Reporting & Mgt

Contract SLA & Warranty Mgt

Diagnosis & Prescription

Reliability, Quality QMS & Safety SMS

Prognosis, Health & Performance Mgt

ConditionMonitoring

Prediction-Failure & Causal Weights

Event, Incident & Case Mgt

Predictive Maintenance

Case Based Reasoning Diagnostics & Prescription

Redesign Respond Monitor Sense IOT

MRO

OPS

Serv

ice C

on

sum

pti

on

& C

ust

om

er

Exp

eri

en

ce M

an

ag

em

en

t

Autonomic Service

Asset Health & Performance Mgmt.

Predictive Analytics Machine Learning Failure Prediction & Causal Weightings

Source: The Internet of Flying Things (pdf)

Example of Cortana Intelligence Suite in actionData Sources Ingest Prepare Analyze Publish Consume

Sensors

and devices

Stream

Analytics Machine

LearningCortana

Business

apps

SQL Data

Warehouse

Diagnostic

Streaming

Power BI

Enterprise

data sources

Azure Blob storage

Data Factory: Move data, orchestrate, schedule and monitor

Data Catalog: Register, annotate, understand, discover data sets

HDInsight

Event

Hubs

HDInsight

Machine

Learning

Stream

Analytics

Predictive Maintenance

Cortana Intelligence Suite

发现连接到各种维修问题的模式

减少运营风险

控制维护成本

提高整体品牌形象

降低库存成本减少不必要且费时的维修作业

讲师的激情因您的鼓励而愈发澎湃，

立即提交反馈即有机会获得精美礼品。