IBM Maximo APM - Predictive MaintenanceInsights SaaS

User Guide

IBM

Note

Before using this information and the product it supports, read the information in “Notices” on page 15.

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iv IBM Maximo APM - Predictive Maintenance Insights SaaS : User Guide

First edition (October 2019)

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vi IBM Maximo APM - Predictive Maintenance Insights SaaS : User Guide

Contents

Chapter 1. Product overview..................................................................................1What's new................................................................................................................................................... 1Accessibility features...................................................................................................................................2

Chapter 2. Configuring data................................................................................... 3Creating device types...................................................................................................................................3Creating physical and logical interfaces......................................................................................................3Configuring the integration with IBM Maximo APM - Asset Health Insights..............................................4Creating asset groups.................................................................................................................................. 4Predictive model templates.........................................................................................................................4Using the model templates..........................................................................................................................5

Chapter 3. Analyzing predictive maintenance data................................................. 7Using predictive model output data............................................................................................................ 7

Chapter 4. Upload CSV file for scoring.................................................................... 9

Chapter 5. Troubleshooting..................................................................................11Error when setting schedule......................................................................................................................11Failure probability curve is not displayed in the user interface................................................................11

Chapter 6. Frequently asked questions................................................................ 13Trademarks................................................................................................................................................ 16Terms and conditions for product documentation................................................................................... 16IBM Online Privacy Statement.................................................................................................................. 17

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Chapter 1. Product overviewIBM® Maximo® APM - Predictive Maintenance Insights SaaS focuses on the needs of reliability engineersto 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 andanalytics 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.

What's newThe following new features and fixes are available in IBM Maximo APM - Predictive Maintenance InsightsSaaS.

New in this release

October 2019

• You can use the new PMI - QuickStart Tutorial Jupyter Notebook to upload data from CSV filesinstead of using data from IBM Maximo Asset Health Insights and Watson™ IoT Platform.

• There are new optional parameters for model instance name and model instance description. Theseparameters are available in Notebooks with names prepended with "PMI -".

• If there is no data, the user interface now displays system information stating there is not enoughdata.

• If one model instance is not activated, the user interface now displays system information statingthat there is not enough data to show the current failure probability.

• The failure path now shows the correct timestamp.• When you display a root-cause analysis (RCA) path, the correct RCA tree displays.• You can use a REST API to upload CSV files for scoring. When you upload a CSV file using the REST

API, the data is loaded into Watson IoT Platform. The next time you score, the data is used forscoring.

• You can download pydoc for PMLIB by using the following call: https://APM_PMI_PROD_URL/ibm/pmi/service/rest/ds/APM_ID/APM_API_KEY/doc/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

© Copyright IBM Corp. 2019 1

APM_API_KEY is an API key that is generated by an IBM Maximo APM - Asset Health Insightssystem administrator.

New in previous releases

August 2019

• You can remove unwanted algorithms from the set of anomaly detection algorithms that are used intraining by using the PMI-Anomaly Detection Notebook.

• The anomaly detection score is now displayed if there is only one score.• The failure probability prediction is now displayed if it is zero.• If the asset name contains a special character, a complete list is now returned for the asset group

mapping.• You can download asset data, sensor data, and failure history in a CSV file from object storage by

using a cloud storage browser. You must have the credentials for the object storage.• The data scientist can train a model by using a CSV file. Instructions are included in a new Notebook

called PMI - Model Development Tutorial.• The data scientist can set up assets and device types and their associations by using CSV files in the

PMI - Model Development Tutorial Notebook.

Accessibility featuresAccessibility features help users who have a physical disability, such as restricted mobility or limitedvision, to use information technology products.

For information about the commitment that IBM has to accessibility, see the IBM Accessibility Center(www.ibm.com/able).

HTML documentation has accessibility features. PDF documents are supplemental and, as such, includeno added accessibility features.

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

Chapter 2. Configuring dataBefore you use IBM Maximo APM - Predictive Maintenance Insights SaaS, you must configure the dataflow into the product and integrate the product with IBM Maximo Asset Health Insights.

Creating device typesEach 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 beginBefore you perform this task, you must have an IBMid so you can log in to Watson IoT Platform. You mustdetermine which sensors are linked with which assets. You must register the sensors with Watson IoTPlatform.

Procedure

1. In Watson IoT Platform, go to the Devices menu and click the Device Types tab. Click Add DeviceType.

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 MaintenanceInsights SaaS welcome letter.

8. Verify the information and then click Done.

Creating physical and logical interfacesYou 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 beginBefore you perform this task, you must have an IBMid so you can log in to Watson IoT Platform. For moreinformation 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. Afteryou add the event types, click Add.

6. Define the physical interface. You can use properties to define the interface behavior and the formatof the data that is presented on devices. When you are finished, click Done.

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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. Click Done.13. Send a few events to trigger the population of data lake metadata, either by using a Watson IoT

Platform device simulator, an application, or a device.

Configuring the integration with IBM Maximo APM - Asset Health InsightsAfter you create the physical and logical interfaces, you configure the integration with IBM Maximo APM -Asset Health Insights.

First, integrate the product with Watson IoT Platform. For more information, see the Integrating withWatson IoT Platform topic in the IBM Maximo APM - Asset Health Insights documentation.

Next, configure IBM Maximo APM - Asset Health Insights to integrate with IBM Maximo APM - PredictiveMaintenance Insights SaaS. For more information, see the How to configure IBM Maximo APM - AssetHealth Insights to integrate with IBM Maximo APM - Predictive Maintenance Insights technote.

Creating asset groupsCreating 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 AssetGroup page.

2. Click Create Asset Group.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 nextAfter you create an asset group, you can get the group ID from the Asset Group page and use the groupID in the models.

Predictive model templatesMaximo APM - Predictive Maintenance Insights SaaS comes with predictive model templates that you canuse 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 failurehistory data. The goal is to build models that can characterize the probability that a particular assetwill fail within a given future prediction window.

Predicted failure datesThis model predicts when the next failures will happen for assets by using IoT sensor data, meterdata, and past failure history data. With this information reliability engineers can determine whetheran asset is well-maintained. This information can be used to adjust the maintenance schedule.

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Anomaly detectionThe anomaly detection model helps identify unusual patterns in the behavior of the asset, whichmight indicate potential failures or pre-failure behaviors.

Failure contribution breakdownIn case of bad performance or equipment failure, domain experts are interested in understanding thecause of the bad outcome. This model provides the domain experts the needed visibility into thecause and factors that cause the undesirable outcome.

Failure probability curveThis model uses historical asset retirement data to estimate the probability of end of life failure for agiven asset. Normally the failure probability goes up as the asset ages.

Using the model templatesMaximo APM - Predictive Maintenance Insights SaaS comes with predictive model templates that aredelivered as Jupyter Notebooks. You can use the templates, customize the templates, or create your ownNotebooks. 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 customNotebook, you must create a GitHub repository that contains your code and include the URL to yourGitHub repository in the Notebook.

Procedure

1. To download the Notebooks in a compressed file, run the following API call from a Web browser:https://APM_PMI_PROD_URL/ibm/pmi/service/rest/ds/APM_ID/APM_API_KEY/file/download?ver=1.0where:APM_PMI_PROD_URL is the URL provided in the welcome letterAPM_ID is the ID provided in the welcome letterAPM_API_KEY is an API key that is generated by an IBM Maximo APM - Asset Health Insights systemadministrator.

2. To open a Notebook in Watson Studio, see the topic Creating notebooks in the Watson Studiodocumentation. Follow the instructions in the Notebook. The Notebooks contain documentation andcode 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 forthe predictive model. For example, for asset group 1001, edit the line to readasset_group_id='1001'.

4. Run the code in each section of the Notebook. When you are finished, the trained model is uploaded toIBM Maximo APM - Asset Health Insights. From there, you can select and enable the model and setthe schedule on which the model runs.

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Chapter 3. Analyzing predictive maintenance dataYou can analyze the results of predictive models in IBM Maximo APM - Predictive Maintenance InsightsSaaS 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 predictivemodel 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 Maintenancetab in the Details view of the asset in the Reliability Engineering Work Center. You can view themaintenance timeline of the asset, including work order details, estimated due dates for plannedmaintenance, 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 HealthInsights to view the overall asset health information, and you can choose follow-up actions, such ascreating a work order, service request, or investigation that is related to maintenance on the asset. Youcan also edit cards in the Reliability Engineering Work Center and select the predictive maintenanceattributes 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 InsightsKnowledge Center.

Related tasksUsing the model templatesMaximo APM - Predictive Maintenance Insights SaaS comes with predictive model templates that aredelivered as Jupyter Notebooks. You can use the templates, customize the templates, or create your ownNotebooks. Notebooks are editable in IBM Watson Studio.Creating asset groupsCreating asset groups enables you to control the assets that you use with models.

Using predictive model output dataThe results from each of the five the predictive maintenance models in Maximo APM - PredictiveMaintenance Insights SaaS provide you with various data that you can evaluate and use to determinewhich maintenance actions to take for assets.

Failure ProbabilityBy using the Failure Probability model results, you can view the probability that the asset will fail within aspecified time period, such as 30 days, the amount that the probability has increased or decreased sincethe 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 viewupcoming maintenance and determine whether scheduled work or preventive maintenance can addressthe problem. You can update existing work orders with the observations from the model. Alternatively, ifno upcoming work orders or preventive maintenance is scheduled, you might create an inspection workorder to investigate likely problems.

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Failure Contribution BreakdownBy using the Failure Contribution Breakdown model results, you can view a breakdown of the asset’sattributes to see how the attributes contribute to the failure probability of the asset. You can also see howattributes contribute to failure in an analysis tree model.

You can use the Failure Contribution Breakdown results or analysis tree to review which asset attributespredict a failure to help technicians or inspectors focus on likely problems. For example, an extremelyhigh probability of failure might require corrective or emergency work to be planned as soon as possible.

Predicted Failure DatesBy using the Predicted Failure Dates model results, you can view the date that the asset is predicted tofail, the scheduled next maintenance date, and the date that the asset was assessed. You can navigate toview more information in the maintenance logs.

If you apply a predictive maintenance strategy to your assets, you can use this model to determine whento create and schedule work to address issues. You can accurately plan the work and organize thenecessary labor, spare parts, and inventory ahead of the predicted failure date, and you can work withyour maintenance team to plan and schedule the work during already scheduled downtime. By using apredictive maintenance strategy, you can reduce the costs that are associated with unnecessarypreventive maintenance.

Anomaly DetectionBy using the Anomaly Detection model results, you can view the latest anomaly score versus the anomalythreshold and the date of the previously detected anomaly. You can navigate to view more information inthe 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 ofthe asset, such as the meter or IoT sensor outputs that this model is analyzing, you can determine whatcause the anomaly. Frequent anomalous behavior for an asset might suggest that some underlying issuerequires further investigation by the maintenance team. A refurbishment or replacement might benecessary, 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. Thecurve 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 growsover time, so you might decide to replace or refurbish the asset, especially if you notice that its health isalso deteriorating. You can also compare information in the curve with other failure prediction models. Ifthe information and the models align, the predicted failures are most likely related to the age of the asset.

Related conceptsPredictive model templatesMaximo APM - Predictive Maintenance Insights SaaS comes with predictive model templates that you canuse to quickly get started with the product.

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Chapter 4. Upload CSV file for scoringUploads a comma-separated values (CSV) file that is used for scoring. When you upload a CSV file, thedata is loaded into Watson IoT Platform. The next time that you score, the data is used for scoring.

URL

APM_API_BASEURL/ibm/pmi/service/file_upload?instanceId=APM_ID&entityType=entityTypeInNotebook

Method

The request type POST

URL parameters

instanceId: String. The APM ID in the Maximo APM - Predictive Maintenance Insights SaaSwelcome letter. Mandatory.

entityType: String. The entityType used in the Jupyter Notebook. Mandatory.

Headers

apmapitoken: APM_API_KEY. The API key, which is used for authentication. Mandatory.

Data parametersNone.

Sample body

Body: form-dataKey=fileValue=test2.csv

Success response200

Sample Curl call

curl -X POST \ '{{APM_API_BASEURL}}/ibm/pmi/service/file_upload?instanceId=xxxxxxxxxxx&entityType=TrainBrakeSensor' \ -H 'Content-Type: multipart/form-data' \ -H 'apmapitoken: xxxxxxxx \ -H 'content-type: multipart/form-data; boundary=--33' \ -F file=@/Users/test2.csv)

Notes

The system uses the apmapitoken to do authentication. If you do not provide an apmapitoken, thesystem refuses your request.

The column names of CVS must match the column names that you use in the pmlib.setup_iot functioncall in the Notebook. For example, in the Notebook, you have

t = pmlib.setup_iot_type( name=iot_type, df=df, columns=[ 'TRAINBRAKESIMULATION_NUMBEROFPASSENGERS', 'TRAINBRAKESIMULATION_HUMIDITY', 'TRAINBRAKESIMULATION_LOAD', 'TRAINBRAKESIMULATION_AXLEVIBRATION', 'TRAINBRAKESIMULATION_TEMP', 'TRAINBRAKESIMULATION_OBSCOND', 'TRAINBRAKESIMULATION_RUNHOURS', 'TRAINBRAKESIMULATION_WHEELSPEED',

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'TRAINBRAKESIMULATION_AXLEMOMENTUM', ], deviceid_column='deviceid', timestamp_column='rcv_timestamp_utc', timestamp_in_payload=True, rename_columns={ 'RCV_TIMESTAMP_UTC': 'customtime', 'TRAINBRAKESIMULATION_NUMBEROFPASSENGERS': 'passengers', 'TRAINBRAKESIMULATION_HUMIDITY': 'humidity', 'TRAINBRAKESIMULATION_LOAD': 'load', 'TRAINBRAKESIMULATION_AXLEVIBRATION': 'axlevibration', 'TRAINBRAKESIMULATION_TEMP': 'temp', 'TRAINBRAKESIMULATION_OBSCOND': 'obscond', 'TRAINBRAKESIMULATION_RUNHOURS': 'runhours', 'TRAINBRAKESIMULATION_WHEELSPEED': 'wheelspeed', 'TRAINBRAKESIMULATION_AXLEMOMENTUM': 'axlemomentum', }, write='deletefirst', use_wiotp=False)

The columns of the CSV file that is used by the API must have the columns' names that are the valuesof the rename_columns dictionary (key:value pair) and DEVICETYPE and DEVICEID columns.

Column names are not case-sensitive.

Here is a sample CSV file:

PASSENGERS,HUMIDITY,LOAD,AXLEVIBRATION,TEMP,OBSCOND,RUNHOURS,WHEELSPEED,AXLEMOMENTUM,DEVICETYPE,DEVICEID,CUSTOMTIME101,35,62,7,46,3,1,43,28,TrainBrakeSensor,TrainBrake_1,2019-07-11 03:24:34

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Chapter 5. TroubleshootingThis section contains solutions to help you troubleshoot common issues.

Error when setting scheduleA "pmi#error" message is displayed when you set the schedule for a model instance in the ReliabilityEngineering Work Center.

To resolve this problem, delete the value for the DISPLAYDATE property in Maximo Asset Management.

Failure probability curve is not displayed in the user interfaceIf you train only the Failure Probability Curve model, you cannot view the failure probability curve in theuser interface.

To resolve this problem, train one more model that is supplied with the product, for example, FailureProbability Prediction, Anomaly Detection, or Predicted Failure Date.

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Chapter 6. Frequently asked questionsFind answers to frequently asked questions.

Table 1. Frequently asked questions

Question Answer

How can I use the timestamp data embedded inmy IoT events as the time base, instead of theWatson IoT Platform time?

You can use any datetime formatted attribute inthe IoT event payload as your time base. You needto create the IoT type by using a Notebook. Formore information, see the PMI - ModelDevelopment Tutorial Notebook.

Can I use multiple sensors per asset? Yes. When you associate assets and devices, addmultiple device attributes of different devices tothe asset. Or, when you bulk load the association,add multiple devices for the asset.

What asset data is supported? • installdate (Maximo standard asset attribute)• estendoflife (Maximo standard asset attribute)• asset failure history from asset work orders that

are not cancelled and have the problemcode andfaildate values set.

What is the accuracy of the models that areincluded with the product?

You can get the accuracy value from notebookswith names that start with "WS". For example, theWS - Failure Probability notebook containsaccuracy information.

If I change or update the logical interface, do I losethe historical data in the data lake?

If you change or update a logical interface, WatsonIoT Platform renames your existing table andcreates a new table for the new logical interface.You can then go to the database and back up andrestore your data.

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

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