Machine Learning Impact on IoT - Part 2
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How to Introduce Machine Learning in a IoT scenario: - Map of ML algorithms and uses cases - Case Study: Jet engines predictive maintenance.
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Machine Learning Impact on IoT - Part 2
- 1. • Contact us for 1 free consultation: giuseppe@valueamplify.com • Twitter: @giuseppeHighTec • Linkedin: www.linkedin.com/in/giuseppemascarella
- 2. What Is Machine Learning for IoT?
- 6. IoT Predictive Maintenance Concepts Predictive Maintenance in IoT Traditional Predicative Maintenance Goal Improve production and/or maintenance efficiency Ensure the reliability of machine operation Data Data stream (time varying features), Multiple data sources Very limited time varying features Scope Component level, System level Parts level Approach Data driven Model driven Tasks Failure prediction, fault/failure detection & diagnosis, maintenance actions recommendation, etc. Essentially any task that improves production/maintenance efficiency Failure prediction (prognosis), fault/failure detection & diagnosis (diagnosis)
- 7. Example Predictive Maintenance Use Cases
- 8. IoT Predictive Maintenance – Qantas Airways ~24,000 sensors Qantas A380 Fleet Technical Delays 12 $65M+ per A380 50% Technical Delays 400-700 Fault/warning messages/day have potential for predictive modelling Develop ML model (MATLAB) alongside local university Optimise code Reduce runtime Develop user web front endBuild evaluation module Refine model parameters Configure model in AML PM template Evaluate & refine model data & parameters Visualize results in Power BI Months /year Orchestrate data pipeline in Azure Data Factory Source: www.microsoft.com
- 9. Stay ahead of the curve with Cortana Intelligence Suite Business apps Custom apps Sensors and devices People Automated systems Data Intelligence Cortana Intelligence Action Apps
- 10. The IoT Ecosystem Around ML Intelligence Dashboards & Visualizations Information Management Big Data Stores Machine Learning and Analytics CortanaEvent Hubs HDInsight (Hadoop and Spark) Stream Analytics Data Intelligence Action People Automated Systems Apps Web Mobile Bots Bot Framework SQL Data WarehouseData Catalog Data Lake Analytics Data Factory Machine Learning Data Lake Store Cognitive Services Power BI Data Sources Apps Sensors and devices Data
- 15. Scope Question is sharp. Data measures what they care about. Data is connected. Data is accurate. A lot of data. The better the raw materials, the better the product. E.g. Predict whether component X will fail in the next Y days; clear path of action with answer E.g. Identifiers at the level they are predicting E.g. Will be difficult to predict failure accurately with few examples E.g. Failures are really failures, human labels on root causes; domain knowledge translated into process E.g. Machine information linkable to usage information
- 21. Data Sources The failure history of a machine or component within the machine. The repair history of a machine, e.g. previous maintenance records, components replaced, maintenance activities performed. Maintenance types. The operation conditions of a machine, e.g. data collected from sensors. FAILURE HISTORY REPAIR HISTORY MACHINECONDITIONS The features of machine or components, e.g. production date, technical specifications. Environmental features that may influence a machine’s performance, e.g. location, temperature, other interactions. The attributes of the operator who uses the machine, e.g. driver. MACHINE FEATURES OPERATING CONDITIONS OPERATORATTRIBUTES
- 22. Sample training data ~20k rows, 100 unique engine id Sample testing data ~13k rows, 100 unique engine id Sample ground truth data 100 rows Please refer to following link of doc for Data description section https://gallery.cortanaintelligence.com/Experiment/df7c518dcba 7407fb855377339d6589f
- 23. Classes •Regression models: How many more cycles an in- service engine will last before it fails? •Binary classification: Is this engine going to fail within w1 cycles? •Multi-class classification: Is this engine going to fail within the window [1, w0] cycles or to fail within the window [w0+1, w1] cycles, or it will not fail within w1 cycles?
- 24. Feature Engineering The process of creating features that provide better or additional predictive power to the learning algorithm. a1 a2 … a21 sd1 sd2 … sd21 RUL label1 label2 40+ engineered features
- 25. Data Labeling How far ahead of time the alert of failure should trigger before the actual failure event.
- 26. Feature Engineering 1. Selected raw features 2. Aggregate features
- 31. Modelling Techniques Predict failures within a future period of time BINARY CLASSIFICATION Predict failures with their causes within a future time period. Predict remaining useful life within ranges of future periods MULTICLASSCLASSIFICATION Predict remaining useful life, the amount of time before the next failure REGRESSION Identify change in normal trends to find anomalies ANOMALYDETECTION
- 32. Data Labeling id cycle … RUL label1 label2 1 1 191 0 0 1 2 190 0 0 1 3 189 0 0 1 4 188 0 0 … … … … 1 160 32 0 0 1 161 31 0 0 1 162 30 1 1 1 163 29 1 1 1 164 28 1 1 1 165 27 1 1 1 166 26 1 1 1 167 25 1 1 1 168 24 1 1 1 169 23 1 1 1 170 22 1 1 1 171 21 1 1 1 172 20 1 1 1 173 19 1 1 1 174 18 1 1 1 175 17 1 1 1 176 16 1 1 1 177 15 1 2 1 178 14 1 2 1 179 13 1 2 1 180 12 1 2 1 181 11 1 2 1 182 10 1 2 1 183 9 1 2 1 184 8 1 2 1 185 7 1 2 1 186 6 1 2 1 187 5 1 2 1 188 4 1 2 1 189 3 1 2 1 190 2 1 2 1 191 1 1 2 1 192 0 1 2 Predefined window size for classification models w1 = 30 w0 = 15 w1 w0 Regression Binary classification Multi-class classification
- 33. Evaluation • Time dependent split • Train in the past, validate in the future • Class imbalance • A few failure events • sampling, cost-sensitive learning • Metrics • Recall, Precision, F1 • Random Guess, Weighted Guess
- 34. “Most IoT data are not used currently… the data that are used today are mostly for anomaly detection and control, not optimization and prediction, which provide the greatest value.”1
- 36. Acknowledgements • We utilized the following publically available data to help us generate realistic data for the demo shown. We received assistance in creating this solution as a result of this repository and the donators of the data: “A. Saxena and K. Goebel (2008). "PHM08 Challenge Data Set", NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA.” • McKinskey Global Institute, The Internet of Things: Mapping the Value beyond the hype • Microsoft Cortana Gallery Experiments
- 37. Learn and try yourself! • Learn from Cortana Analytics Gallery • Solution package material – deploy by hand to learn here • Try Cortana Analytics Solution Template – Predictive Maintenance for Aerospace in private preview • Try Azure IOT pre-configured solution for Predictive Maintenance • Read the Predictive Maintenance Playbook for more details on how to approach these problems • Run the Modelling Guide R Notebook for a DS walk- through
- 38. • Contact us for 1 free consultation: giuseppe@valueamplify.com • Twitter: @giuseppeHighTec • Linkedin: www.linkedin.com/in/giuseppemascarella
Editor's Notes
- Designing with artificial intelligence The secret to getting people to engage with products and services is to make interaction as simple as possible. Remove friction and people will embrace your product. But simplicity isn’t the same as minimalism. The secret to getting people to engage with products and services is to make interaction as simple as possible. Remove friction and people will embrace your product. But simplicity isn’t the same as minimalism. For IoT devices, the interface may be as minimal as a few LEDs and a touchpad—and that kind of minimalism can feel obscure and confusing to users. What’s more, IoT devices often need to operate in concert to create delightful services, such as coordinating the levels of light and sound in a room. This simply increases complexity. Unless we come up with new ideas, the world is about to feel terribly broken. That’s why interfaces and services increasingly rely on artificial intelligence technologies. Algorithms make sense of contextual data, anticipate user needs, and accept more natural forms of input, like voice commands. Keeping the interface simple means the device has to become more intelligent. AI isn’t magic—it’s engineering. To develop compelling products, designers and product managers need to understand the constraints and possibilities of AI. They also need to develop new ways of working together so that the resulting products and services feel more… human. This session looks at how algorithms work, examines what they can and can’t do, and explores case studies and examples of how product teams have combined a deep understanding of people with clever design and smart algorithms to produce truly wonderful products. Decisions of what data to keep, ignore, and what to forward to a centralized authority will be required. Many of the kinetic devices will be used and application whose action can neither tolerate long latency nor risk the possibility that the connection with the centralized authority (“the cloud”) is not available. Their decisions must be made instantly with local information and knowledge. Most IoT endpoints will be limited in capabilities due to size, cost, and the power requirements and will need companion computing that is either embedded in the larger system or in a companion gateway. These gateways will primarily bridge between the local device communication domains and higher level network domains and will in most cases make behavioral decisions. As the industry matures, these gateways will also be responsible for allowing data to be exchanged between intended devices, and ensuring the information is protected. Network traffic patterns will be significantly impacted as more device-to-endpoint traffic will occur and more machine-to-machine communication will materialize, shifting from today’s patterns. However, these solutions will not be static, and their evolving behavior will need to vary depending on local characteristics, giving rise to more software-defined functions at both the edge and within the datacenter. Further, their numbers will be vast and their operation cannot require human intervention.
- The input data consists of "train_FD001.txt", "test_FD001.txt", and "RUL_FD001.txt" in the original data source [1]. The training data ("train_FD001.txt") 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. Each engine is assumed to start with different degrees of initial wear and manufacturing variation, and this information is unknown to the user. In this simulated data, the engine is assumed to be operating normally at the start of each time series. It starts to degrade at some point during the series of the operating cycles. The degradation progresses and grows in magnitude. When a predefined threshold is reached, then the engine is considered unsafe for further operation. In other words, the last cycle in each time series can be considered as the failure point of the corresponding engine. Taking the sample training data shown in the following table as an example, the engine with id=1 fails at cycle 192, and engine with id=2 fails at cycle 287. The testing data ("test_FD001.txt") has the same data schema as the training data. The only difference is that the data does not indicate when the failure occurs (in other words, the last time period does NOT represent the failure point). Taking the sample testing data shown in the following table as an example, the engine with id=1 runs from cycle 1 through cycle 31. It is not shown how many more cycles this engine can last before it fails. The ground truth data ("RUL_FD001.txt") provides the number of remaining working cycles for the engines in the testing data. Taking the sample ground truth data shown in the following table as an example, the engine with id=1 in the testing data can run another 112 cycles before it fails.
- Selected raw features The raw features are those that are included in the original input data. In order to decide which raw features should be included in the training data, both the detailed data field description and domain knowledge is helpful. In this template, all the sensor measurements (s1-s21) are included in the training data. Other raw features get used are: cycle, setting1-setting3. Aggregate features These features summarize the historical activity of each asset. In the template, two types of aggregate features are created for each of the 21 sensors. The description of these features are shown below. a1-a21: the moving average of sensor values in the most w recent cycles sd1-sd21: the standard deviation of sensor values in the most w recent cycles