NEAL ANALYTICS AZURE IOT INSTRUCTIONAL KIT V1.0
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- Blaise Lloyd
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1 NEAL ANALYTICS AZURE IOT INSTRUCTIONAL KIT V1.0
2 This document is provided as-is. Information and views expressed in this document, including URL and other Internet Web site references, may change without notice. You bear the risk of using it. This document does not provide you with any legal rights to any intellectual property in any Microsoft product. You may copy and use this document for your internal, reference purposes Microsoft. All rights reserved.
3 Table of Contents Introduction... 5 Purchasable Kit... 6 Literature Review... 7 Hardware... 7 Raspberry Pi... 7 Arduino UNO Board... 7 Bread Board... 7 Digital Temperature Sensor... 8 Accelerometer... 8 Azure Services... 8 Azure Event Hub... 8 Azure Machine Learning... 9 Azure Stream Analytics... 9 Setting up the Device Required Materials Configure the Devices Hardware Assembly Access to Azure services Software Prerequisites Signing up for the Windows Azure Service Creating Azure Event Hub Deploying the Website Creating the Blob Storage Azure Stream Analytics Creating Alert Jobs Creating Azure ML Jobs Creating Power BI Jobs Azure Machine Learning Power BI Appendix Alert Jobs... 86
4 Arduino_Temp Arduino_Vib Azure ML Jobs ML_Arduino_Temp ML_Arduino_Vib Azure ML Jobs PowerBI_Arduino_Temp PowerBI_Arduino_Temp PowerBI_Arduino_Vib PowerBI_Arduino_Vib References Machine Learning Stream Analytics Service Bus and Event Hubs Power BI... 94
5 Introduction In this project there are code samples, configuration scripts and guides that will help you set up devices (Arduino and Raspberry Pi) and sensors (Accelerometer and Digital Temperature sensor), and configure Microsoft Azure services like Azure Event Hub, Stream Analytics, Azure Machine Learning and Power BI to view and analyze the data produced by those devices. It can be built quickly and easily with minimal knowledge of programming or Microsoft Azure, using commodity devices available locally or online - for example an Arduino UNO board, connected to a Raspberry Pi sending data to an Azure website.
6 Purchasable Kit The Arduino and Raspberry kit required for the project can be purchased here. The Kit will include all the Devices and Software required for the proceedings of this course. It will also include a Three months free license to the Azure services which should be more than enough to complete this course. The Kit will contain the following: Raspberry Pi 2 Quad-Core 900 MHz 1GB RAM Quick-Start Guide CanaKit WiFi Adapter / Dongle (Ralink RT5370 chipset) 8 GB MicroSD Card - Raspberry Pi Recommended MicroSD Card with NOOBS High Quality Raspberry Pi 2 Case (Clear or Black available) CanaKit 2.5A Micro USB Power Supply (UL Listed) specially designed for the Raspberry Pi 2 Premium Quality HDMI Cable (6-foot) A handy GPIO Quick Reference Card CanaKit General Assembly Guide for Beginners to Electronics Arduino Uno R3 3 USB Cable Arduino Uno Enclosure SparkFun ProtoShield Kit One Set of Arduino Stackable Headers Mini Breadboard Assorted 32-pcs Jumper Wires Keyes DS18B20 Digital Temperature Sensor Module for Arduino Ethernet Cable 3 Months Free of Azure Note: This Kit will come with programs and codes preinstalled in these devices which will save you time doing it yourself. Purchasing this kit will help you skip the first few steps of loading the software and codes into the devices and jump directly to Hardware Assembly. Also be sure to check out our Internet of Things video series. This 6-part video series provides great introduction to the hardware and software that comes with the Kit.
7 Literature Review Hardware Raspberry Pi The Raspberry Pi is a low cost, credit-card sized computer that plugs into a computer monitor or TV, and uses a standard keyboard and mouse. It is a capable little device that enables people of all ages to explore computing, and to learn how to program in languages like Scratch and Python. It s capable of doing everything you d expect a desktop computer to do, from browsing the internet and playing high-definition video, to making spreadsheets, word-processing, and playing games. Arduino UNO Board The Arduino Uno is a microcontroller board based on the ATmega328. It has 14 digital input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a 16 MHz ceramic resonator, a USB connection, a power jack, an ICSP header, and a reset button. It contains everything needed to support the microcontroller. It can be powered via the USB connection or with an external power supply. Bread Board A breadboard (or protoboard) is a construction base for prototyping of electronics. Because the solderless breadboard does not require soldering, it is reusable. This makes it easy to use for creating temporary prototypes and experimenting with circuit design. For this reason, solderless breadboards are also extremely popular with students and in technological education. Horizontal rows are linked together and so are the vertical holes as shown in the figure below.
8 Jump wires (also called jumper wires) for solderless breadboarding can be obtained in ready-to-use jump wire sets. Ready-to-use jump wires come in different qualities, some even with tiny plugs attached to the wire ends. Differently colored wires and color-coding discipline are often adhered to for consistency. Digital Temperature Sensor This is an electronic thermometer which has high accuracy over a wide range (accurate to ±0.5 C over the range of -10 C to +85 C) (Workable from -55 C to +125 C). You can locate these thermometer chips up to 100M away from your Arduino. Shorter cables can be just 2 wires. NOTE: There must be a pull-up resistor of about 5K in all cases, but the Brick versions have this included. To hook up your sensor, plug it into a breadboard. Connect the 5v of your Arduino to the middle prong. Attach the ground of your Arduino to the leftmost prong on the circuit board (marked with a minus). Connect the digital 2 port to the right most prong on the circuit board (marked with an S). Accelerometer The MX2125 has a chamber of gas with a heating element in the center and four temperature sensors around its edge. When the accelerometer is level, the hot gas pocket rises to the top-center of the chamber, and all the sensors will measure the same temperature. By tilting the accelerometer, the hot gas will be collected closer to some of temperature sensors. By comparing the sensor temperatures, both static acceleration (gravity and tilt) and dynamic acceleration (like taking a ride in a car) can be detected. The MX2125 converts the temperature measurements into signals (pulse durations) that are easy for microcontrollers to measure and decipher. Azure Services Azure Event Hub Event Hub is a highly scalable publish-subscribe ingestor that can intake millions of events per second so that you can process and analyze the massive amounts of data produced by your connected devices and applications. Once collected into Event Hubs you can transform and store data using any real-time analytics provider or with batching/storage adapters. Event Hubs provide easy provisioning of capacity to ingest events from millions of devices while preserving event order on a per device basis.
9 Azure Machine Learning Azure Machine Learning offers a streamlined experience for all data scientist skill levels, from setting up with only a web browser, to using drag and drop gestures and simple data flow graphs to set up experiments. Machine Learning Studio features a library of time-saving sample experiments, R and Python packages. Azure ML also supports R and Python custom code, which can be dropped directly into your workspace. Azure Stream Analytics Stream Analytics is an event processing engine that helps uncover real-time insights from devices, sensors, infrastructure, applications and data. It will enable various opportunities including Internet of Things (IoT) scenarios such as real-time fleet management or gaining insights from devices like mobile phones or connected cars.
10 Setting up the Device Required Materials 1. Arduino OSEPP TM UNO R3 Plus 2. Raspberry Pi 1 3. Arduino prototype shield v.5 4. Accelerometer Mx DS18B20 Digital Temperature Sensor 6. Jumper Wires a. Red (5V) b. Yellow c. Black (Gnd) d. Green 7. USB A to B Cable 8. Power Cable and Ethernet Cable 9. B&W International type 1000 Case
11 Configure the Devices 1. To install the Arduino software for the Temperature sensor and the Mx2125 Accelerator combo, go to Here and follow the instructions. 2. You can download the code from Here. 3. To install the Raspberry Pi software, go to Here and follow the instructions 4. The code for the Raspberry Pi, is available Here
12 Hardware Assembly 1. Take the Arduino board. The Pin diagram of the Arduino OSEPP TM UNO R3 Plus is on the right. RC Connector RC Pins Digital Pins DC Power Jack Power & Reset Analog Input Pins 2. Take the Arduino prototype shield v.5 and connect its pins into the Arduino OSEPP TM UNO R3 Plus board as shown in the Picture at the right. 3. Place the Accelerometer Mx2125 at the middle of the bread board with 3 pins being on the either side of the board. Pin Name Function 1 Temp Temperature Out 2 Yout Y-axis PWM output 3 GND Ground -> 0V 4 GND Ground -> 0 V 5 Xout X-axis PWM output 6 VDD Input voltage: +3.3 to +5 VDC 4. Place the Keyes Temperature sensor on the bread board right of the Accelerometer. +5V Signal Gnd
13 5. Now let s connect the wires. Wires must be connected in the following way: a. Connect the Yellow wire from the Signal pin of Temperature Sensor to the Digital Pin 4 of the Arduino board b. Connect the Yellow wire from the Yout of the Accelerometer to the Digital Pin 3 of the Arduino board c. Connect the Green wire from the Xout of the Accelerometer to the Digital Pin 2 of the Arduino board d. Connect the Red wire from the +5V Pin of the Accelerometer to the +5V Pin among the Power and Reset pins of the Arduino board e. Connect the Red wire from the +5V Pin of the Temperature Sensor to the +5V Pin of the Accelerometer f. Connect the Black wire from the Gnd pin of the Accelerometer to the Gnd Pin among the Power and Reset pins of the Arduino board g. Connect the Black wire from the Gnd pin of the Temperature Sensor to the Gnd Pin of the Accelerometer 6. Connect the Arduino to the Raspberry Pi using the USB A to B cable. Make sure it is USB 2.0 not 3.0, as the 3.0 connector does not fit the Arduino 7. Connect the power cord to the Raspberry Pi and switch it ON
14 Access to Azure services Software Prerequisites 1. Microsoft Azure subscription (Click Here) 2. Access to the Azure Streaming Analytics Preview 3. Visual Studio 2013 Community Edition or above Signing up for the Windows Azure Service 1. Open the link Click on My Account at the top and enter your user name and password if you already have created the account. (Skip Step 2) If you don t, sign up for a new one by clicking Free Trial and follow the instructions on the screen. 2. Once you have created your new trial account, Click on the My Account at the top if you had not in the previous Step.
15 3. Click on the Management Portal 4. Click on the Subscription at the top and a menu will pop up. Click on Manage your Account option as shown at the right. 5. Enter your credentials and log in. 6. Click on any of your subscription from what you see in the list. You can see an instructional video here. Or, you can create a new subscription by clicking on Add Subscription and follow the instructions on the screen.
16 7. Click on Partner Information 8. Here is where you would enter and verify the MPN ID and then submit. Enter as MPN ID. Click on check ID and a green check mark will appear. Click submit when done. Creating Azure Event Hub Event Hubs is a highly scalable publish-subscribe ingestor that can intake millions of events per second so that you can process and analyze the massive amounts of data produced by your connected devices and applications. Once collected into Event Hubs you can transform and store data using any real-time analytics provider. To create the Azure event hubs, go Here and follow the instructions Deploying the Website To deploy a sample website that show data and alerts as shown in the picture below, go Here and follow the instructions. The output will appear on the hosted website as shown below.
17 Creating the Blob Storage 9. Open the link Enter your user name and password if you already have created the account. If you don t, sign up at Windows Azure. 10. Open the Azure Management Portal. Click on the + sign at the bottom left corner.
18 11. Click on Data Services -> Storage -> Quick Create 12. Enter the following: URL Location/Affinity Subscription Replication arduinovibtemp Central US Pay-As-You-Go Locally Redundant Click on Create Stream Analytics Job at the bottom right corner. 13. Click on arduinovibtemp under Storage. 14. Click on Containers and then click on Create a Container.
19 15. Enter ehdevices under Name and Select Private under Access -> Next.
20 Azure Stream Analytics The instructions below will help you setup the Stream Analytics queries or go to the link Stream Analytics. Note: These queries are hard-coded to the data streams defined in the "starter kit" in this project, meaning the same JSON string contents, etc. Also note that the SQL queries ARE CASE SENSITIVE, so that "temperature" <> "TEMPERATURE". You should make sure that the spelling and case of the incoming measure names are the same as in the SQL queries. Creating Alert Jobs 1. Open the link /. Enter your user name and password if you already have created the account. If you don t, sign up at Windows Azure. 2. Open the Azure Management Portal. Click on the + sign at the bottom left corner. 3. Click on Data Services -> Stream Analytics -> Quick Create -> Job name Arduino_Temp.
21 4. Click on Create Stream Analytics Job at the bottom right corner. 5. Click on Arduino_Temp. 6. Click on Add an Input. 7. Select Data Stream -> Next.
22 8. Select Event Hub -> Next. 9. Enter Input Alias name as Devices. Select Use Event Hub from Current Subscription as Subscription. Select cooler1-ns in Service Bus Namespace and ehdevices in Event Hub Name. Select RootManagerSharedAccessKey as Policy Name. Click Next.
23 10. Select JSON for Event Serialization Format and click Next. 11. Click on Add an Output. 12. Click on Event Hub -> Next
24 13. Select Use Event Hub from Current Subscription as Subscription. Select cooler1-ns in Service Bus Namespace and ehdevices in Event Hub Name. Select RootManagerSharedAccessKey as Policy Name. Click Next. 14. Select JSON for Event Serialization Format and click Next. 15. Click on Query. Copy Paste the contents from the Appendix Link. Click Save. Copy/Paste
25 16. Click on Dash board and click Start to start the job. 17. Click on the + sign at the bottom left corner. 18. Click on Data Services -> Stream Analytics -> Quick Create -> Job name Arduino_Vib. 19. Click on Create Stream Analytics Job at the bottom right corner.
26 20. Click on Arduino_Vib. 21. Click on Add an Input. 22. Select Data Stream -> Next.
27 23. Select Event Hub -> Next. 24. Enter Input Alias name as DevicesInput. Select Use Event Hub from Current Subscription as Subscription. Select cooler1-ns in Service Bus Namespace and ehdevices in Event Hub Name. Select RootManagerSharedAccessKey as Policy Name. Click Next.
28 25. Select JSON for Event Serialization Format and click Next. 26. Click on Add an Output. 27. Click on Event Hub -> Next
29 28. Select Use Event Hub from Current Subscription as Subscription. Select cooler1-ns in Service Bus Namespace and ehdevices in Event Hub Name. Select RootManagerSharedAccessKey as Policy Name. Click Next. 29. Select JSON for Event Serialization Format and click Next.
30 30. Click on Query. Copy Paste the contents from the Appendix Link. Click Save. Copy/Paste 31. Click on Dash board and click Start to start the job. 32. Now go to the website you hosted, and you will be able to see the alerts being shown up there as shown in the picture below. Note: It might take few minutes before you start seeing the alerts.
31 Alerts Creating Azure ML Jobs 1. Open the link Enter your user name and password if you already have created the account. If you don t, sign up at Windows Azure. 2. Click on the + sign at the bottom left corner.
32 3. Click on Data Services -> Stream Analytics -> Quick Create -> Job name ML_Arduino_Vib. 4. Click on Create Stream Analytics Job at the bottom right corner. 5. Click on ML_Arduino_Vib under Stream Analytics. 6. Click on Add an Input.
33 7. Select Data Stream -> Next. 8. Select Event Hub -> Next.
34 9. Enter Input Alias name as InputVib. Select Use Event Hub from Current Subscription as Subscription. Select cooler1-ns in Service Bus Namespace and ehdevices in Event Hub Name. Select RootManagerSharedAccessKey as Policy Name. Click Next. 10. Select JSON for Event Serialization Format and click Next. 11. Click on Add an Output.
35 12. Click on Blob storage -> Next 13. Select Use Event Hub from Current Subscription as Subscription. Select arduinovibtemp in Storage Account and ehdevices in Container. Enter vib under Filename Prefix. Click Next.
36 14. Select CSV for Event Serialization Format and click Next. 15. Click on Query. Copy Paste the contents from the Appendix Link. Click Save. Copy/Paste 16. Click on Dash board and click Start to start the job.
37 17. Click on the + sign at the bottom left corner. 18. Click on Data Services -> Stream Analytics -> Quick Create -> Job name ML_Arduino_Temp. 19. Click on Create Stream Analytics Job at the bottom right corner. 20. Click on ML_Arduino_Temp under Stream Analytics. 21. Click on Add an Input.
38 22. Select Data Stream -> Next. 23. Select Event Hub -> Next.
39 24. Enter Input Alias name as InputTemp. Select Use Event Hub from Current Subscription as Subscription. Select cooler1-ns in Service Bus Namespace and ehdevices in Event Hub Name. Select RootManagerSharedAccessKey as Policy Name. Click Next. 25. Select JSON for Event Serialization Format and click Next. 26. Click on Add an Output.
40 27. Click on Blob storage -> Next 28. Select Use Event Hub from Current Subscription as Subscription. Select arduinovibtemp in Storage Account and ehdevices in Container. Enter temp under Filename Prefix. Click Next.
41 29. Select CSV for Event Serialization Format and click Next. 30. Click on Query. Copy Paste the contents from the Appendix Link. Click Save. Copy/Paste 31. Click on Dash board and click Start to start the job.
42 Creating Power BI Jobs 1. Open the link Enter your user name and password if you already have created the account. If you don t, sign up at Windows Azure. 2. Click on the + sign at the bottom left corner. 3. Click on Data Services -> Stream Analytics -> Quick Create -> Job name PowerBI_Arduino_Temp1. 4. Click on Create Stream Analytics Job at the bottom right corner.
43 5. Click on PowerBI_Arduino_Temp1. 6. Click on Add an Input. 7. Select Data Stream -> Next.
44 8. Select Event Hub -> Next. 9. Enter Input Alias name as DevicesInp. Select Use Event Hub from Current Subscription as Subscription. Select cooler1-ns in Service Bus Namespace and ehdevices in Event Hub Name. Select RootManagerSharedAccessKey as Policy Name. Click Next.
45 10. Select JSON for Event Serialization Format and click Next. 11. Click on Add Add an Output. 12. Click on Power BI -> Next 13. Click on Authorize Now if you already have a Power BI account. If not, create a new one by clicking on Sign up now and follow the steps.
46 14. Type in Temp1 as the data set name and Temp2 as the table name. Click Next. 15. Click on Query. Copy Paste the contents from the Appendix Link. Click Save. Copy/Paste
47 16. Click on Dash board and click Start to start the job. 17. Click on the + sign at the bottom left corner. 18. Click on Data Services -> Stream Analytics -> Quick Create -> Job name PowerBI_Arduino_Temp Click on Create Stream Analytics Job at the bottom right corner.
48 20. Click on PowerBI_Arduino_Temp Click on Add an Input. 22. Select Data Stream -> Next.
49 23. Select Event Hub -> Next. 24. Enter Input Alias name as DevicesIn. Select Use Event Hub from Current Subscription as Subscription. Select cooler1-ns in Service Bus Namespace and ehdevices in Event Hub Name. Select RootManagerSharedAccessKey as Policy Name. Click Next.
50 25. Select JSON for Event Serialization Format and click Next. 26. Click on Add Add an Output. 27. Click on Power BI -> Next 28. Click on Authorize Now if you already have a Power BI account. If not, create a new one by clicking on Sign up now and follow the steps.
51 29. Type in Temp3 as the data set name and Temp4 as the table name. Click Next. 30. Click on Query. Copy Paste the contents from the Appendix Link. Click Save. Copy/Paste
52 31. Click on Dash board and click Start to start the job. 32. Click on the + sign at the bottom left corner. 33. Click on Data Services -> Stream Analytics -> Quick Create -> Job name PowerBI_Arduino_Vib Click on Create Stream Analytics Job at the bottom right corner.
53 35. Click on PowerBI_Arduino_Vib Click on Add an Input. 37. Select Data Stream -> Next.
54 38. Select Event Hub -> Next. 39. Enter Input Alias name as InpDevices. Select Use Event Hub from Current Subscription as Subscription. Select cooler1-ns in Service Bus Namespace and ehdevices in Event Hub Name. Select RootManagerSharedAccessKey as Policy Name. Click Next.
55 40. Select JSON for Event Serialization Format and click Next. 41. Click on Add Add an Output. 42. Click on Power BI -> Next 43. Click on Authorize Now if you already have a Power BI account. If not, create a new one by clicking on Sign up now and follow the steps.
56 44. Type in Vib1 as the data set name and Vib2 as the table name Click Next. 45. Click on Query. Copy Paste the contents from the Appendix Link. Click Save. Copy/Paste 46. Click on Dash board and click Start to start the job.
57 47. Click on Data Services -> Stream Analytics -> Quick Create -> Job name PowerBI_Arduino_Vib Click on Create Stream Analytics Job at the bottom right corner. 49. Click on PowerBI_Arduino_Vib Click on Add an Input.
58 51. Select Data Stream -> Next. 52. Select Event Hub -> Next.
59 53. Enter Input Alias name as InDevices. Select Use Event Hub from Current Subscription as Subscription. Select cooler1-ns in Service Bus Namespace and ehdevices in Event Hub Name. Select RootManagerSharedAccessKey as Policy Name. Click Next. 54. Select JSON for Event Serialization Format and click Next. 55. Click on Add Add an Output.
60 56. Click on Power BI -> Next 57. Click on Authorize Now if you already have a Power BI account. If not, create a new one by clicking on Sign up now and follow the steps. 58. Type in Vib3 as the data set name and Vib4 as the table name Click Next.
61 59. Click on Query. Copy Paste the contents from the Appendix Link. Click Save. Copy/Paste 60. Click on Dash board and click Start to start the job.
62 61. Now that you have started all four Power BI jobs, and that you have seen the alerts showing up on the website you hosted, we can now go ahead and create our dashboards.
63 Azure Machine Learning 1. Open the link Azure ML. Enter your user name and password if you already have created the account. If you don t, sign up. 2. Click on New at the bottom left corner. 3. Click on Experiment -> Click on Blank Experiment
64 4. Change the name of the Experiment to Arduino Vib Test Anomaly Detection 5. In the left menu expand, click on the Data Input and Output -> Click on the Reader module and drag it into the workspace. Drag 6. In the left menu expand, click on the Data Input and Output -> Click on the Reader module and drag it into the workspace. Drag 7. In the left menu expand, click on the Data Transformation -> Click on Manipulation -> Click on the Project Columns module and drag it into the workspace. Drag
65 8. Connect the output of Reader module to the input of the Project Columns module by putting focus on the circle in the Reader and dragging it to the circle on the Project Columns module. 9. In the left menu expand, click on the Data Transformation -> Click on Manipulation -> Click on the Project Columns module and drag it into the workspace. Drag 10. Connect the output of Reader module to the input of the Project Columns module by putting focus on the circle in the Reader and dragging it to the circle on the Project Columns module. 11. In the left menu expand, click on the Data Transformation -> Click on Manipulation -> Click on the Add Columns module and drag it into the workspace. Drag 12. Connect the output of Project Columns module on the left to the left input of the Add Columns module by putting focus on the circle in the Project Columns and dragging it to the circle on the Add Columns module. Similarly, connect the output of Project Columns module on the right to the right input of the Add Columns module by putting focus on
66 the circle in the Project Columns and dragging it to the circle on the Add Columns module. 13. In the left menu expand, click on the Machine Learning -> Click on Train - > Click on the Train Anomaly Detection Model module and drag it into the workspace. Drag 14. In the left menu expand, click on the Machine Learning -> Click on Initialize Model -> Click on the Anomaly Detection -> Click on One- Class Support Vector Machine module and drag it into the workspace. Drag 15. Connect the output of One-Class Support Vector Machine module to the left input of the Train Anomaly Detection Model module by putting focus on the circle in the One-Class Support Vector Machine and dragging it to the circle on the Train Anomaly Detection Model module. Connect the output of Add Columns module to the right input of the Train Anomaly Detection Model module by putting focus on the circle in the Add Columns and dragging it to the circle on the Train Anomaly Detection Model module.
67 16. In the left menu expand, click on the Machine Learning -> Click on Score - > Click on Score Model module and drag it into the workspace. Drag 17. Connect the output of Train Anomaly Detection module to the left input of the Score Model module by putting focus on the circle in the Train Anomaly Detection Model and dragging it to the circle on the Score Model module. Connect the output of Add Columns module to the right input of the Score Model module by putting focus on the circle in the Add Columns and dragging it to the circle on the Score Model module. 18. In the left menu expand, click on the Data Format Conversions -> Click on Convert to CSV module and drag it into the workspace. Drag
68 19. Connect the output of Score Model module to the Convert to CSV module by putting focus on the circle in the Score Model and dragging it to the circle on the Convert to CSV module. Click on Save at the bottom 20. Open the link Enter your user name and password if you already have created the account. If you don t, sign up at Windows Azure. 21. Click on Storage -> Click on arduinovibtemp -> Click on Manage Access Key 22. Now go back to Azure ML and click on Reader module on the left. Copy paste the Account Name, Account Key from the Manage Access Key into the Reader module in the Azure ML. Enter ehdevices/vib/ as the path to the container and check the box file has header.
69 23. Now go back to Azure ML and click on Reader module on the right. Copy paste the Account Name, Account Key from the Manage Access Key into the Reader module in the Azure ML. Enter ehdevices/temp/ as the path to the container and check the box file has header. 24. Click on the Project Columns at the left and then click on the Launch Column Selector 25. Type vib and press enter. Click on the Next at the bottom right corner. 26. Click on the Project Columns at the right and then click on the Launch Column Selector
70 27. Type temp and press enter. Click on the Next at the bottom right corner. 28. Click on Save at the bottom. Click on Run. 29. After running the model, a green checkmark appears on all modules.
71 30. You can click on the circle in the Score Model module and a menu will pop out. Click on Visualize. 31. If Scored Labels are 0, then there is no Anomaly and if they are 1, that means it is an anomaly. 32. You can also, click on the circle in the Convert to CSV module and a menu will pop out. Click on Download and you can then save the output as a CSV file on to your desktop.
72 Power BI To show the outputs in a Power BI dashboard, follow the steps below. 1. To create dashboards in Power BI, click Here and log in with your credentials. You will notice that the datasets we created in the output of PowerBI_Arduino_Vib1 (Vib1), PowerBI_Arduino_Vib2 (Vib3), PowerBI_Arduino_Temp1 (Temp1), And PowerBI_Arduino_Temp2 (Temp3) will appear here under Datasets as shown in the picture at the right. 2. Now Click on Vib1 under Datasets.
73 3. Select the value from the fields list on the right side of the report builder. Drag the field into the center of the report builder. Drag 4. Click on the drop-down arrow to the right of the column in the Value box and change it to an average instead of a sum. 5. Select the timecreated from the fields list on the right side of the report builder. Drag the field into the Axis box. Drag
74 6. Select the KPI Line from the Visualize menu at the bar on the left. 7. Click on SAVE at the left top of the window and type PowerBI_Vib1 and click on SAVE button in the command box. 8. Now Click on Vib3 under Datasets.
75 9. Select the vibmax from the fields list on the right side of the report builder. Drag the field into the center of the report builder. Drag 10. Click on the drop-down arrow to the right of the column in the Value box and change it to an average instead of a sum. 11. Select the timecreated from the fields list on the right side of the report builder. Drag the field into the Axis box. Drag 12. Select the alerttype from the fields list on the right side of the report builder. Drag the field into the center of the report builder. Note: Vibmax might get selected by itself. Drag
76 13. Select the vibmax from the Value box on the right side of the report builder. Drag the field into the Axis box. Select the alerttype from the Axis box on the right side of the report builder. Drag the field into the Value box. Drag Drag 14. Select the KPI Tree Map from the Visualize menu at the bar on the left. 15. Click on SAVE at the left top of the window and type PowerBI_Vib2 and click on SAVE button in the command box.
77 16. Now Click on Temp1 under Datasets. 17. Select the value from the fields list on the right side of the report builder. Drag the field into the center of the report builder. Drag
78 18. Click on the drop-down arrow to the right of the column in the Value box and change it to an average instead of a sum. 19. Select the timecreated from the fields list on the right side of the report builder. Drag the field into the Axis box. Drag 20. Select the KPI Line from the Visualize menu at the bar on the left.
79 21. Click on SAVE at the left top of the window and type PowerBI_Temp1 and click on SAVE button in the command box. 22. Now Click on Temp3 under Datasets. 23. Select the tempmax from the fields list on the right side of the report builder. Drag the field into the center of the report builder. Drag
80 24. Click on the drop-down arrow to the right of the column in the Value box and change it to an average instead of a sum. 25. Select the timecreated from the fields list on the right side of the report builder. Drag the field into the Axis box. Drag 26. Select the alerttype from the fields list on the right side of the report builder. Drag the field into the center of the report builder. Note: Tempmax might get selected by itself. Drag
81 27. Select the tempmax from the Value box on the right side of the report builder. Drag the field into the Axis box. Select the alerttype from the Axis box on the right side of the report builder. Drag the field into the Value box. Drag Drag 28. Select the KPI Tree Map from the Visualize menu at the bar on the left.
82 29. Click on SAVE at the left top of the window and type PowerBI_Temp2 and click on SAVE button in the command box. 30. To create a dashboard, click on the plus sign beside the Dashboards. Name the dashboard as PowerBI_Vib_Temp 31. Click on PowerBI_Vib1 under the Reports 32. Select the visualization. Click on the top right corner of the graph as shown in the figure to pin the graph onto the dashboard.
83 33. Click on PowerBI_Vib2 under the Reports 34. Select the visualization. Click on the top right corner of the graph as shown in the figure to pin the graph onto the dashboard. 35. Select the next visualization. Click on the top right corner of the graph as shown in the figure to pin the graph onto the dashboard.
84 36. Click on PowerBI_Temp1 under the Reports 37. Select the visualization. Click on the top right corner of the graph as shown in the figure to pin the graph onto the dashboard. 38. Click on PowerBI_Temp2 under the Reports 39. Select the visualization. Click on the top right corner of the graph as shown in the figure to pin the graph onto the dashboard.
85 40. Select the next visualization. Click on the top right corner of the graph as shown in the figure to pin the graph onto the dashboard. 41. Arrange and size as appropriate. The finished PowerBI Report should look like the picture below.
86 Appendix Alert Jobs Arduino_Temp SELECT 'TemperatureSpike' AS alerttype, 'Temperature over 22' AS message, displayname, guid, measurename, unitofmeasure, location, organization, MIN(timecreated) AS timecreated, MAX(value) AS TempMax, MAX(value) AS value FROM Devices TIMESTAMP BY timecreated WHERE measurename = 'temperature' OR measurename = 'Temperature' GROUP BY displayname, guid, measurename, unitofmeasure, location, organization, TumblingWindow(Second, 5) HAVING TempMax > 23
87 Arduino_Vib SELECT 'VibrationSpike' AS alerttype, 'vibration over 900' AS message, displayname, guid, measurename, unitofmeasure, location, organization, MIN(timecreated) AS timecreated, MAX(value) AS VibMax, MAX(value) AS value FROM DevicesInput TIMESTAMP BY timecreated WHERE measurename = 'acceleration_tot' OR measurename = 'Acceleration_tot' GROUP BY displayname, guid, measurename, unitofmeasure, location, organization, TumblingWindow(Second, 5) HAVING VibMax > 900
88 Azure ML Jobs ML_Arduino_Temp SELECT 'TemperatureSpike' AS alerttype, 'Temperature over 22' AS message, displayname, guid, measurename, unitofmeasure, location, organization, MIN(timecreated) AS timecreated, value AS temp FROM InputTemp TIMESTAMP BY timecreated WHERE measurename = 'temperature' OR measurename = 'Temperature' GROUP BY displayname, guid, measurename, unitofmeasure, location, organization, value, TumblingWindow(Second, 5)
89 ML_Arduino_Vib SELECT 'VibrationSpike' AS alerttype, 'vibration over 900' AS message, displayname, guid, measurename, unitofmeasure, location, organization, MIN(timecreated) AS timecreated, value AS vib FROM InputVib TIMESTAMP BY timecreated WHERE measurename = 'acceleration_tot' OR measurename = 'Acceleration_tot' GROUP BY displayname, guid, measurename, unitofmeasure, location, organization, value, TumblingWindow(Second, 5)
90 Azure ML Jobs PowerBI_Arduino_Temp1 SELECT 'TemperatureSpike' AS alerttype, 'Temperature over 22' AS message, displayname, guid, measurename, unitofmeasure, location, organization, MIN(timecreated) AS timecreated, value FROM DevicesInp TIMESTAMP BY timecreated WHERE measurename = 'temperature' OR measurename = 'Temperature' GROUP BY displayname, guid, measurename, unitofmeasure, location, organization, value, TumblingWindow(Second, 5)
91 PowerBI_Arduino_Temp2 SELECT 'TemperatureSpike' AS alerttype, 'Temperature over 22' AS message, displayname, guid, measurename, unitofmeasure, location, organization, MIN(timecreated) AS timecreated, MAX(value) AS TempMax, MAX(value) AS value FROM DevicesIn TIMESTAMP BY timecreated WHERE measurename = 'temperature' OR measurename = 'Temperature' GROUP BY displayname, guid, measurename, unitofmeasure, location, organization, TumblingWindow(Second, 5) HAVING TempMax > 23
92 PowerBI_Arduino_Vib1 SELECT 'VibrationSpike' AS alerttype, 'vibration over 900' AS message, displayname, guid, measurename, unitofmeasure, location, organization, MIN(timecreated) AS timecreated, value FROM InpDevices TIMESTAMP BY timecreated WHERE measurename = 'acceleration_tot' OR measurename = 'Acceleration_tot' GROUP BY displayname, guid, measurename, unitofmeasure, location, organization, value, TumblingWindow(Second, 5)
93 PowerBI_Arduino_Vib2 SELECT 'VibrationSpike' AS alerttype, 'vibration over 900' AS message, displayname, guid, measurename, unitofmeasure, location, organization, MIN(timecreated) AS timecreated, MAX(value) AS VibMax, MAX(value) AS value FROM InDevices TIMESTAMP BY timecreated WHERE measurename = 'acceleration_tot' OR measurename = 'Acceleration_tot' GROUP BY displayname, guid, measurename, unitofmeasure, location, organization, TumblingWindow(Second, 5) HAVING VibMax > 900
94 References Machine Learning Azure Machine Learning Frequently Asked Questions (FAQ) Import your training data into Azure Machine Learning Studio Top 10 Tips and Tricks for using Azure Machine Learning tips-and-tricks-for-using-azure-machine-learning/ Stream Analytics Get started using Azure Stream Analytics Azure Service Bus and Event Hubs Event Hubs - Cloud big data solutions Microsoft Azure How to create a Service Bus Namespace and an Event Hub using a PowerShell script Power BI Microsoft Power BI Portal Power BI Blog Forrester positions Microsoft as a leader in Agile Business Intelligence report
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