IBM Maximo APM - Predictive Maintenance Insights SaaS. User Guide IBM

Transcription

1 IBM Maximo APM - Predictive Maintenance Insights SaaS User Guide IBM

2

3 Note Before using this information and the product it supports, read the information in Notices on page 9. Copyright IBM Corp iii

4 First edition (March 2019) iv IBM Maximo APM - Predictive Maintenance Insights SaaS : User Guide

5 Contents Chapter 1. Product overview...1 Accessibility features...1 Chapter 2. Configuring data... 3 Creating device types...3 Creating physical and logical interfaces...3 Configuring the integration with IBM Maximo APM - Asset Health Insights...4 Creating asset groups... 4 Predictive model templates...5 Using the model templates...5 Chapter 3. Analyzing predictive maintenance data... 7 Using predictive model output data... 7 Trademarks Terms and conditions for product documentation IBM Online Privacy Statement v

6 vi

7 Chapter 1. Product overview IBM Maximo APM - Predictive Maintenance Insights SaaS focuses on the needs of reliability engineers to identify and manage asset reliability risks that might adversely affect plant or business operations. Maximo APM - Predictive Maintenance Insights SaaS enables organizations to apply machine learning and analytics to the operational data that is generated by critical assets. Through these capabilities, reliability, manufacturing, operations, and maintenance personnel in asset-intensive industries can: Quickly assess performance of critical assets to help plan and prioritize maintenance needs. Determine which assets are being well-maintained and use prescriptive analysis to optimize maintenance practices. Identify operational factors that positively and negatively affect asset performance. Use this information to guide maintenance strategy. Examine performance of assets, including attributes, risk factors, maintenance logs, and predicted time to failure. Use detailed insight to prescribe maintenance strategies. Obtain the probability of failure within a configurable time window. Identify the primary factors contributing to the next predicted failure. Detect anomalous behavior for assets that might be operating abnormally. Generate a failure probability curve of an asset. Accessibility features Accessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products. For information about the commitment that IBM has to accessibility, see the IBM Accessibility Center ( HTML documentation has accessibility features. PDF documents are supplemental and, as such, include no added accessibility features. Copyright IBM Corp

8 2 IBM Maximo APM - Predictive Maintenance Insights SaaS : User Guide

9 Chapter 2. Configuring data Before you use IBM Maximo APM - Predictive Maintenance Insights SaaS, you must configure the data flow into the product and integrate the product with IBM Maximo Asset Health Insights. Creating device types Each device that is connected to the IBM Watson IoT Platform must be associated with a device type. Device types are groups of devices that share common characteristics. Before you begin Before you perform this task, you must have an IBMid so you can log in to Watson IoT Platform. You must determine which sensors are linked with which assets. You must register the sensors with Watson IoT Platform. Procedure 1. In Watson IoT Platform, go to the Devices menu and click the Device Types tab. Click Add Device Type. 2. Enter a name and description for the device type and then click Next. 3. Optionally enter more information about the device type and then click Done. 4. Click Register Devices to register devices with this device type. 5. Enter a device ID in the Device ID field and then click Next. 6. Add information about the device and then click Next. 7. In the Authentication Token field, enter the Watson IoT Platform API token and then click Next. You can get the Watson IoT Platform API token from the Maximo APM - Predictive Maintenance Insights SaaS welcome letter. 8. Verify the information and then click Done. Creating physical and logical interfaces You must create a physical interface and logical interfaces for your devices so that Maximo APM - Predictive Maintenance Insights SaaS can receive data when a sensor sends data to Watson IoT Platform. Before you begin Before you perform this task, you must have an IBMid so you can log in to Watson IoT Platform. For more information about physical and logical interfaces, see the Watson IoT Platform documentation. Procedure 1. In Watson IoT Platform, go to the Devices page and click the Device Types tab. 2. Select a device type to expand the details of the device type. Click the Interface tab. Click Next. 3. Click Create Physical Interface. 4. Enter a name and description for the physical interface and then click Next. 5. Select one or more event types. To add an event type, click Create event type and then add event types to the physical interface. You can select events, import them, or create them manually. After you add the event types, click Add. 6. Define the physical interface. You can use properties to define the interface behavior and the format of the data that is presented on devices. When you are finished, click Done. Copyright IBM Corp

10 7. Click Create Logical Interface. 8. Specify a name, alias, and description for the logical interface and then click Next. 9. Click Add Property. Add a property and then click Next. 10. For Select when you want notifications to be sent, click For All Events and then click Done. 11. Click Activate. 12. To confirm the device type configuration, click Deploy. Configuring the integration with IBM Maximo APM - Asset Health Insights After you create the physical and logical interfaces, you configure the integration with IBM Maximo APM - Asset Health Insights. Before you begin Before you begin this task, get the Cloudant URL, user name, and password from Watson IoT Platform. In the Watson IoT Platform dashboard, select the Usage tab. Next to Cloudant NoSQL DB Service, click View Details. On the Cloudant NoSQL DB card, click View more. Also, generate an API key and API token in Watson IoT Platform. Procedure 1. In the Reliability Engineering Work Center of Maximo APM - Asset Health Insights, select the IoT connection service page. 2. For Select an IoT Service, click IBM Watson IoT Platform and then click Next. 3. Under IBM Watson IoT Platform, for URL, enter the URL of your Watson IoT Platform service. Append /api/v0002/ to the URL. For example, abcdef.internetofthings.ibmcloud.com/api/v0002/ You can get the Watson IoT Platform URL from the Maximo APM - Predictive Maintenance Insights SaaS welcome letter. 4. For User Name, enter an API key from Watson IoT Platform. 5. For Password, enter an API token from Watson IoT Platform. 6. Click Test Connection to verify that your connection is successful. 7. Under IBM Cloudant, enter the URL, user name, and password. Use the host value for the URL in Maximo APM - Asset Health Insights. 8. Click Test Connection to verify that your connection is successful. 9. Click Next to configure end points. 10. Accept the default settings for end point properties and click Next to register device types. 11. Select the device type that you created and then click Register as Asset. Click Next. 12. Select the Associate Devices tab. Select Assets > Associate Assets. 13. Choose the asset. Select the Device Type menu and select the device type that you want to associate with the asset. Select the Device ID menu and specify the device ID. Repeat this process for all assets that you need to associate with a device. Creating asset groups Creating asset groups enables you to control the assets that you use with models. Procedure 1. In the Reliability Engineering Work Center of Maximo APM - Asset Health Insights, select the Asset Group page. 2. Click Create Asset Group. 4 IBM Maximo APM - Predictive Maintenance Insights SaaS : User Guide

11 3. Enter a group name and optional description. Select Applies To Asset and then click Next. 4. Use a query to choose the assets that you want to associate with this asset group. When you have the list of assets, click Save. What to do next After you create an asset group, you can get the group ID from the Asset Group page and use the group ID in the models. Predictive model templates Maximo APM - Predictive Maintenance Insights SaaS comes with predictive model templates that you can use to quickly get started with the product. The product includes the following templates: Failure probability prediction This model predicts imminent failures for assets by using IoT sensor data, meter data, and past failure history data. The goal is to build models that can characterize the probability that a particular asset will fail within a given future prediction window. Predicted failure dates This model predicts when the next failures will happen for assets by using IoT sensor data, meter data, and past failure history data. With this information reliability engineers can determine whether an asset is well-maintained. This information can be used to adjust the maintenance schedule. Anomaly detection The anomaly detection model helps identify unusual patterns in the behavior of the asset, which might indicate potential failures or pre-failure behaviors. Failure contribution breakdown In case of bad performance or equipment failure, domain experts are interested in understanding the cause of the bad outcome. This model provides the domain experts the needed visibility into the cause and factors that cause the undesirable outcome. Failure probability curve This model uses historical asset retirement data to estimate the probability of end of life failure for a given asset. Normally the failure probability goes up as the asset ages. Using the model templates Maximo APM - Predictive Maintenance Insights SaaS comes with predictive model templates that are delivered as Jupyter Notebooks. You can use the templates, customize the templates, or create your own Notebooks. Notebooks are editable in IBM Watson Studio. About this task This task describes how to use the Notebooks that are supplied with the product. To create a custom Notebook, you must create a GitHub repository that contains your code and include the URL to your GitHub repository in the Notebook. Procedure 1. To download the Notebooks in a compressed file, run the following API call: download?ver=1.0 where: APM_PMI_PROD_URL is the URL provided in the welcome letter APM_ID is the ID provided in the welcome letter Configuring data 5

12 APM_API_KEY is an API key that is generated by an IBM Maximo APM - Asset Health Insights system administrator. 2. To open a Notebook in Watson Studio, see the topic Creating notebooks in the Watson Studio documentation. Follow the instructions in the Notebook. The Notebooks contain documentation and code that guide you to install prerequisites and perform necessary tasks. 3. In the Notebook, locate the asset_group_id line and list the asset group that you want to use for the predictive model. For example, for asset group 1001, edit the line to read asset_group_id='1001'. 4. Run the code in each section of the Notebook. When you are finished, the trained model is uploaded to IBM Maximo APM - Asset Health Insights. From there, you can select and enable the model and set the schedule on which the model runs. 6 IBM Maximo APM - Predictive Maintenance Insights SaaS : User Guide

13 Chapter 3. Analyzing predictive maintenance data You can analyze the results of predictive models in IBM Maximo APM - Predictive Maintenance Insights SaaS to determine how to complete maintenance for assets. Before you can run a model and analyze the data, you must configure the data, set up the predictive model templates, and define asset groups to identify the assets that are included in the analysis. After you run a predictive maintenance model, you view the output data on the Predictive Maintenance tab in the Details view of the asset in the Reliability Engineering Work Center. You can view the maintenance timeline of the asset, including work order details, estimated due dates for planned maintenance, dates and details of past failures, and predicted next failure dates. You also can view the results of the following Predictive Maintenance models: Failure Probability Failure Contribution Breakdown Predicted Failure Dates Anomaly Detection Failure Probability Curve After you view and analyze the predictive maintenance data, you can use IBM Maximo APM - Asset Health Insights to view the overall asset health information, and you can choose follow-up actions, such as creating a work order, service request, or investigation that is related to maintenance on the asset. You can also edit cards in the Reliability Engineering Work Center and select the predictive maintenance attributes that are displayed on the cards. For more information about asset health cards, see "Configuring the Reliability Engineering Work Center" in the Maximo APM - Asset Health Insights Knowledge Center. Related tasks Using the model templates Maximo APM - Predictive Maintenance Insights SaaS comes with predictive model templates that are delivered as Jupyter Notebooks. You can use the templates, customize the templates, or create your own Notebooks. Notebooks are editable in IBM Watson Studio. Creating asset groups Creating asset groups enables you to control the assets that you use with models. Using predictive model output data The results from each of the five the predictive maintenance models in Maximo APM - Predictive Maintenance Insights SaaS provide you with various data that you can evaluate and use to determine which maintenance actions to take for assets. Failure Probability By using the Failure Probability model results, you can view the probability that the asset will fail within a specified time period, such as 30 days, the amount that the probability has increased or decreased since the previous assessment, and the average failure probability for all assets in the group. For example, if the model is predicting a high probability of failure in the next 60 days, you might view upcoming maintenance and determine whether scheduled work or preventive maintenance can address the problem. You can update existing work orders with the observations from the model. Alternatively, if no upcoming work orders or preventive maintenance is scheduled, you might create an inspection work order to investigate likely problems. Copyright IBM Corp

14 Failure Contribution Breakdown By using the Failure Contribution Breakdown model results, you can view a breakdown of the asset s attributes to see how the attributes contribute to the failure probability of the asset. You can also see how attributes contribute to failure in an analysis tree model. You can use the Failure Contribution Breakdown results or analysis tree to review which asset attributes predict a failure to help technicians or inspectors focus on likely problems. For example, an extremely high probability of failure might require corrective or emergency work to be planned as soon as possible. Predicted Failure Dates By using the Predicted Failure Dates model results, you can view the date that the asset is predicted to fail, the scheduled next maintenance date, and the date that the asset was assessed. You can navigate to view more information in the maintenance logs. If you apply a predictive maintenance strategy to your assets, you can use this model to determine when to create and schedule work to address issues. You can accurately plan the work and organize the necessary labor, spare parts, and inventory ahead of the predicted failure date, and you can work with your maintenance team to plan and schedule the work during already scheduled downtime. By using a predictive maintenance strategy, you can reduce the costs that are associated with unnecessary preventive maintenance. Anomaly Detection By using the Anomaly Detection model results, you can view the latest anomaly score versus the anomaly threshold and the date of the previously detected anomaly. You can navigate to view more information in the anomaly score history details. You can use the results from this model to monitor the behavior of your assets. If an anomaly is detected, it indicates that there is some deviation from normal behavior for this asset. If you know the attributes of the asset, such as the meter or IoT sensor outputs that this model is analyzing, you can determine what cause the anomaly. Frequent anomalous behavior for an asset might suggest that some underlying issue requires further investigation by the maintenance team. A refurbishment or replacement might be necessary, depending on the age of the asset or where the asset exists on the failure probability curve. Failure Probability Curve The Failure Probability Curve shows the probability that an asset will fail at certain points in time. The curve measures retirement and failure rates of similar assets against the age of the selected asset. You can use the curve to estimate how likely your asset is to fail as it gets older. The risk of failure grows over time, so you might decide to replace or refurbish the asset, especially if you notice that its health is also deteriorating. You can also compare information in the curve with other failure prediction models. If the information and the models align, the predicted failures are most likely related to the age of the asset. Related concepts Predictive model templates Maximo APM - Predictive Maintenance Insights SaaS comes with predictive model templates that you can use to quickly get started with the product. 8 IBM Maximo APM - Predictive Maintenance Insights SaaS : User Guide

15 Notices This information was developed for products and services offered in the US. This material might be available from IBM in other languages. However, you may be required to own a copy of the product or product version in that language in order to access it. IBM may not offer the products, services, or features discussed in this document in other countries. Consult your local IBM representative for information on the products and services currently available in your area. Any reference to an IBM product, program, or service is not intended to state or imply that only that IBM product, program, or service may be used. Any functionally equivalent product, program, or service that does not infringe any IBM intellectual property right may be used instead. However, it is the user's responsibility to evaluate and verify the operation of any non-ibm product, program, or service. IBM may have patents or pending patent applications covering subject matter described in this document. The furnishing of this document does not grant you any license to these patents. You can send license inquiries, in writing, to: IBM Director of Licensing IBM Corporation North Castle Drive, MD-NC119 Armonk, NY US For license inquiries regarding double-byte character set (DBCS) information, contact the IBM Intellectual Property Department in your country or send inquiries, in writing, to: Intellectual Property Licensing Legal and Intellectual Property Law IBM Japan Ltd , Nihonbashi-Hakozakicho, Chuo-ku Tokyo , Japan INTERNATIONAL BUSINESS MACHINES CORPORATION PROVIDES THIS PUBLICATION "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Some jurisdictions do not allow disclaimer of express or implied warranties in certain transactions, therefore, this statement may not apply to you. This information could include technical inaccuracies or typographical errors. Changes are periodically made to the information herein; these changes will be incorporated in new editions of the publication. IBM may make improvements and/or changes in the product(s) and/or the program(s) described in this publication at any time without notice. Any references in this information to non-ibm websites are provided for convenience only and do not in any manner serve as an endorsement of those websites. The materials at those websites are not part of the materials for this IBM product and use of those websites is at your own risk. IBM may use or distribute any of the information you provide in any way it believes appropriate without incurring any obligation to you. Licensees of this program who wish to have information about it for the purpose of enabling: (i) the exchange of information between independently created programs and other programs (including this one) and (ii) the mutual use of the information which has been exchanged, should contact: IBM Director of Licensing IBM Corporation North Castle Drive, MD-NC119 Armonk, NY US Copyright IBM Corp

16 Such information may be available, subject to appropriate terms and conditions, including in some cases, payment of a fee. The licensed program described in this document and all licensed material available for it are provided by IBM under terms of the IBM Customer Agreement, IBM International Program License Agreement or any equivalent agreement between us. The performance data and client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions. Information concerning non-ibm products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not tested those products and cannot confirm the accuracy of performance, compatibility or any other claims related to non-ibm products. Questions on the capabilities of non-ibm products should be addressed to the suppliers of those products. This information is for planning purposes only. The information herein is subject to change before the products described become available. This information contains examples of data and reports used in daily business operations. To illustrate them as completely as possible, the examples include the names of individuals, companies, brands, and products. All of these names are fictitious and any similarity to actual people or business enterprises is entirely coincidental. COPYRIGHT LICENSE: This information contains sample application programs in source language, which illustrate programming techniques on various operating platforms. You may copy, modify, and distribute these sample programs in any form without payment to IBM, for the purposes of developing, using, marketing or distributing application programs conforming to the application programming interface for the operating platform for which the sample programs are written. These examples have not been thoroughly tested under all conditions. IBM, therefore, cannot guarantee or imply reliability, serviceability, or function of these programs. The sample programs are provided "AS IS", without warranty of any kind. IBM shall not be liable for any damages arising out of your use of the sample programs. Trademarks IBM, the IBM logo, and ibm.com are trademarks or registered trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at "Copyright and trademark information" at Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates. Linux is a trademark of Linus Torvalds in the United States, other countries, or both. Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both. UNIX is a registered trademark of The Open Group in the United States and other countries. Terms and conditions for product documentation Permissions for the use of these publications are granted subject to the following terms and conditions. Applicability These terms and conditions are in addition to any terms of use for the IBM website. 10 Notices

17 Personal use You may reproduce these publications for your personal, noncommercial use provided that all proprietary notices are preserved. You may not distribute, display or make derivative work of these publications, or any portion thereof, without the express consent of IBM. Commercial use You may reproduce, distribute and display these publications solely within your enterprise provided that all proprietary notices are preserved. You may not make derivative works of these publications, or reproduce, distribute or display these publications or any portion thereof outside your enterprise, without the express consent of IBM. Rights Except as expressly granted in this permission, no other permissions, licenses or rights are granted, either express or implied, to the publications or any information, data, software or other intellectual property contained therein. IBM reserves the right to withdraw the permissions granted herein whenever, in its discretion, the use of the publications is detrimental to its interest or, as determined by IBM, the above instructions are not being properly followed. You may not download, export or re-export this information except in full compliance with all applicable laws and regulations, including all United States export laws and regulations. IBM MAKES NO GUARANTEE ABOUT THE CONTENT OF THESE PUBLICATIONS. THE PUBLICATIONS ARE PROVIDED "AS-IS" AND WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. IBM Online Privacy Statement IBM Software products, including software as service solutions, ( Software Offerings ) may use cookies or other technologies to collect product usage information, to help improve the end user experience, to tailor interactions with the end user or for other purposes. In many cases no personally identifiable information is collected by the Software Offerings. Some of our Software Offerings can help enable you to collect personally identifiable information. If this Software Offering uses cookies to collect personally identifiable information, specific information about this offering s use of cookies is set forth below. Depending upon the configurations deployed, this Software Offering may use session and persistent cookies that collect each user s name, user name, password, or other personally identifiable information for purposes of session management, authentication, single sign-on configuration or other usage tracking or functional purposes. These cookies can be disabled, but disabling them will also likely eliminate the functionality they enable. If the configurations deployed for this Software Offering provide you as customer the ability to collect personally identifiable information from end users via cookies and other technologies, you should seek your own legal advice about any laws applicable to such data collection, including any requirements for notice and consent. For more information about the use of various technologies, including cookies, for these purposes, see IBM s Privacy Policy at and IBM's Online Privacy Statement at in the section entitled Cookies, Web Beacons and Other Technologies and the "IBM Software Products and Software-as-a-Service Privacy Statement" at software/info/product-privacy. Notices 11

18 12 IBM Maximo APM - Predictive Maintenance Insights SaaS : User Guide

19

20 IBM Part Number: (1P) P/N: