sap pdms что это
Описание системы SAP Predictive Maintenance and Service
Информация о SAP Predictive Maintenance and Service
Средний бизнес, Корпорация
Мобильное устройство, Сервер предприятия, Облако (SaaS)
Веб-браузер, iOS, Android
Краткий обзор SAP Predictive Maintenance and Service
SAP Predictive Maintenance and Service – это программная система, повышающая эффективность процессов ТОиР в компании, реализуя соврменные концепции Промышленного интернета вещей (IIoT) и Цифровизации.
Программное обеспечение SAP Predictive Maintenance & Service (рус. САП Предиктивное Техобслуживание и Сервис) от компании SAP SE предназначена для прогнозирования неисправностей оборудования на основе данных от датчиков оборудования, обрабатываемых в корпоративном облаке в реальном времени. Технология SAP SE, таким образом, является инструментом цифровой трансформации компании, позволяет улучшить процессы технического обслуживания и ремонта (ТОиР) и осуществлять, далее, оптимальное управление корпоративными ресурсами и активами.
Программный продукт SAP Predictive Maintenance & Service является частью портфельного решения SAP Intelligent Asset Management. При помощи сервиса SAP PdMS становится возможным объединить данные датчиков оборудования с бизнес-информацией корпоративных систем: системы управления корпоративными активами (EAM), системы планирования ресурсов предприятия (ERP), системы управления взаимоотношениями с клиентами (CRM) и системами дополненной реальности.
Цифровое решение SAP Predictive Maintenance & Service позволяет бизнесу достигнуть следующих преимуществ:
Снижение расходов на техническое обслуживание. Позволяет планировать и изменять графики технического обслуживания динамически, улучшая использование ресурсов и сокращая время простоя активов.
Увеличение доступности физических активов. Позволяет расширить возможности операторов активов и сервисных организаций с помощью больших данных, помогая заранее прогнозировать сбои и осуществлять корректирующее техническое обслуживание.
Повышение рентабельности услуг. Позволяет производителям оборудования и сервисным организациям повысить доходность услуг по удалённому обслуживанию и снизить риски поломки обслуживаемого оборудования, что достигается за счёт обеспечения прозрачности технического состояния удалённх активов.
SAP LEONARDO IoT – PDMS – Predictive Maintenance Service Configuration
Overview:
Purpose:
This PDMS system will capture the real time data and it will alert or notify the reliability engineer if there are any failure is going to happen in future based on the previous failures.
Author:
1. SAP Leonardo IOT – PDMS Architecture:
2 . SAP Cloud Platform Cockpit:
SAP Cloud Platform is an enterprise platform-as-a-service (enterprise PaaS) that provides comprehensive application development services and capabilities, which lets you build, extend, and integrate business applications in the cloud.
SAP Cloud Platform Cockpit is a web-based administration interface tool that provides access to number of functions for configuring and managing applications, services, and subaccounts.
This cockpit also helps us to manage resources, services, security, monitor application metrics, and perform actions on cloud applications.
Get the credentials from your admin or register yourself.
Cloud platform cockpit —> Global accounts —> Sub account —> Subscriptions—> Choose the PDMS tile and click on “go to application” link.
This will open the PDMS FLP.
3. PDMS – PREDICTIVE MAINTENANCE SERVICE CONFIGURATION
PDMS URL looks like
3.1 External System creation and Mapping :
Configure all the required externals system.
Administrator—>Application settings—> External system —> Configure all external system with the required details.
The indicator details will be imported in to Leonardo Cockpit only after successful configuration of this step.
[please refer to the sap portal to get the list of external system and its attribute details.
Note :-
3.2 PDMS – Indicator Creation:
Click on MASTER DATA —> Templates–>
Create the required indicators and assign to indicator group
Make sure that your indicator external id is mapped with right system.
You can find these external ID’s in Leonardo Cockpit- [Refer section 6]
Follow the Indicator Id / group Id naming conversions. Since few char values only taken in Leonardo digital twin creation.(ThingType and Thing).
If you get the external system details then you can conclude that external systems are configured perfectly for the indicator.
3.3 Alert Type Creation:
The alert type allows you to define the alerts that are based equipment errors. It also define associations with an indicator and possible failure modes. The association with failure mode data allows you to identify associations like instructions.
Templates—> Alert Types —> Create alert types & Groups.
Then assign the alerts types in to groups based on the category and the requirement.
3.4 Attribute creation :
Templates—>Attributes—>feed the attribute information with conditions.
Then assign the attributes in to attribute groups.
Create spare part and location template and assign the attribute groups.
Create model /Equipment template and assign the attribute & indicator groups.
3.5 Configure the failure modes:
The Failure mode is helping operator to identify the root cause of the failure based on historical data. This failure mode has been shared by OEM or OPERATOR or SERVICE PROVIDER.Based on user authorization they can edit or create their own failure modes.Failure mode will get triggered based on the rule engine configuration.
Home –> Failure Modes.
3.6 Instruction Creation:
Manufacturer shares the instruction to operator to perform the activities if there is breakdown or maintenance or operation or disposal.
3.7 Rule Engine configuration:
You can use the “RULES tile” to configure the rule engine for alerting or notifying the user if there are any deviation in the captured sensor values.
Note: This rule engine configuration is not relate with machine learning.
3.8 Model Creation:
A model is an abstract representation that is derived from model template. It maintains all the maintenance information.
Here you can assign the instruction, failure modes, alert types, documents, spare parts…
Based on this model user can go ahead to create the equipment.
3.9 Equipment Creation:
Equipment is a physical instance of a model.
IoT SYNC flag should be checked to get the data from the end client.
Make sure that naming conversion should be maintained. Since Leonardo cockpit will take only few characters for “Thing” and “Thing Modeler” creation.[“Thing” is the virtual representation of the equipment].please refer Leonardo iOT section for more info]
[Will discuss about configuring the “MACHINE LEARNING” section end of this document.]
4 SAP CLOUD PLATFORM INTERNET OF THINGS (IOT) CONFIGURATION:
In this section we are going to see …
4.1 HCP User Authorization:
Login with your ROOT Login account credentials and create the separate TENANT.
And also create the user id and password for the users.
Then assign the created user as a admin or member based on the need.
The Internet of Things Service Device Management offers an API that provides functionality for the management of the lifecycle of IoT devices. So based on your convenience and authorization you can go ahead to create the components through API or TOOL.
4.2 Capability Creation:
Capabilities are one the which you are read the it from the sensors.
Create the capability and properties for each equipment.
Login with the newly created account.
Click on the Home —>device management—>capabilities section.
Make sure that the data type and Units should match with PDMS / LEONARDO property values. Otherwise mapping will not work when you integrate this sensor capabilities with PDMS indicator values.
4.3 Create Sensor Types and capability Assignment:
Home —> Device Management—> Sensor Types.
Create the sensor types and assign the right capability group (…ie your indicator group which is created in PDMS) to sensor types.
4.4 Device and Sensor on-boarding Manually with Gateway:
This Gateway component is responsible for collecting data from a sensor network and sending commands to the network.
Here we are going to create a physical node of a device with the right gateway (MQTT/REST) protocol.
After a device has been on-boarded, you can download the device certificate to connect securely to the Internet of Things Gateway Cloud MQTT and REST.
4.5 Sensor creation:
As part of sensor creation, assign the sensor type which we have created earlier. So all the capabilities which are assigned to that sensor types will also appear in that sensor.
4.6 Generate and Download device certificate:
As part of this SAP Cloud Platform IoT configuration, Physical device needs to handshake with and IoT device.
For that we need to download the device certificate from on-boarded device in the IoT and upload in to physical device.
Home—>Tenant—>Device Management—> Devices—> Choose the device —>Click on Certificate Tab—>Choose the certificate type as P12 and generate it.
And also you can see the downloaded certificate in your browser / download folder.
5 Send Data from EDGE – postman – End Client:
Now it’ time to send the data from end client (Physical device) ie.. our simulated edge client to IoT cockpit. For that we need to upload the P12 certificate which have downloaded from IoT cockpit in to End client.
5.1 Upload the downloaded certificate:
Click on chrome browser—>Preferences—>Manage certificate—>This will open your KEY-CHAIN.
5.2 Import the P-12 certificate in to key-chain:
Enter the secret password which you have noted when you upload the device certificate in to keychain.
5.3 Prepare the payload:
Get the alternative ID’s of the device /capability / sensors in IoT Cockpit.
5.4 Posting sensor measured values from the EDGE:
Then click on send button to submit the data to the IoT cockpit. You will get certificate pop up.
Choose the right certificate of the device and click on OK.
Check once again all the configuration settings then close all the browser and sessions.
Then try again. You will get the success response status code as “200” or “202”.modify the number values and send further data to SAP Cloud Platform Internet of Things.
5.5 View sensor Data in SAP Cloud Platform – IoT Cockpit:
Open the IoT cockpit with your credentials.
Then go to Home —>Tenant—>Device Management—>Devices—> choose Your Device—> Click on Data visualization—>Choose the sensor and its properties.
The values will be displayed on the chart / Table.
Now we are ready to consume this sensor data in SAP Leonardo also from iOT Cockpit.
6 SAP LEONARDO-IOT Configuration:
Open the IoT cockpit with your credentials.
Then go to Home —>Tenant—>Device Management—>Devices—> choose Your Device—> Click on Data visualization—>Choose the sensor and its properties.
The values will be displayed on the chart / Table.
Login in to SAP Cloud platform cockpit move in to Global Account—> sub account —> click on the right sub account—> Subscriptions—>Then click Go To Application in SAP LEONARDO IOT section.
This will open the LEONARDO portal.
Then click on Thing Modeler TILE.
From this “Thing Modeler” we can create the digital twins of physical assets directly.
Leonardo platform will collect and process the sensor data from IoT cockpit.
Here you can invoke the customized UI5 application also which is hosted on HCP.
6.1 Mapping sensor data between IoT cockpit and Leonardo cockpit:
6.2 Connectivity creation with IoT Cockpit:
The system will display all the devices and sensors which are all created in IoT cockpit.
Choose the right device and sensor.
Give any mapping name then perform the “Thing” and “sensor” data mapping.
You can see the green color if the Leonardo Thing properties and Sensor capabilities are matched. Otherwise this will be displayed as grey color with blank mapping indication. Click on SAVE Button to proceed further.
Note :
This will consume the data from IoT sensor cockpit and pass it to PDMS.
Now it’s time to post the “measure values” from End client.
(Please refer Posting the data from postman section ):
7 Data Flow verification:
Now you can view the sensor data which you are passing from postman in all the below three platform.
EDGE – postman – End Client:
SAP HCP-IoT Cockpit:
Tenant–>Device Management–>Devices–>Choose your device–>Data Visualization–>select sensor–>Choose the capability.
Leonardo Cockpit:
SAP LEONARDO portal–>Home–>Thing Modeler–>Choose the package–>Thing–>Measured Values.
PDMS indicator chart: 
PDMS—> Equipment—>Master Data—>Monitoring—>Indicators.
Leonardo—>Thing Engineer OData—>Thing Type—>Thing—>Measured Values.
SAP HCP IoT cockpit—>Tenant—>Device Management—>Devices—>Data visualization
PDMS indicator:
8. Machine Learning Engine Configuration:
This section will help you to predict the critical failures. For that we need to configure the dataset and the model with right machine learning algorithm. Then Train the model and score it. You may need to have the MACHINE LEARNING expert to configure this section.
8.1 Health Indicator Dataset Creation:
Create the data set for the health indicator configuration.
Use the data as much as possible. More data will improve the accuracy of the failure prediction.
In this sample we are using TEC algorithm to train our model.
your input data size(row) will vary for train the model.
For TEC [Tree Ensemble Classification], we have chosen labels and add our failure modes which are all part of this Heat Exchanger equipment. Configure the “lead time” and “prediction window”.
8.2 Configure Health Indicator Model Management:
As part of this model configuration, you need to give the data set as input and choose the right machine learning algorithm from the dropdown.
Unit of measure- None
Use those indicators in your output mapping.
There are around 10 algorithms are available in the standard package. You can go ahead choose the right one based on your need.
Choose the date range or schedule a job for train the model. once the training is completed then you can view the status in green color. After the training you can download the training input data and verify your feeds.
Once the training is completed, you can proceed to score this model.
You can find your scoring results in the log summary.
8.3 Verify the Accuracy of the Health Indicator Model:
Feed the sensor data from the “end client” with healthy and unhealthy data.
In this example I am using TEC algorithm to predict our HEAT Exchanger failure.
If you are using classification algorithms ( Logistic regression / Tree Ensemble classification) then train the model with healthy (0) & unhealthy data (1) as part of sensor input.
For Example :
For train the model we have passed the sensor values with the 0 and 1.
Healthy DATA – :[85,88,285,265,0],
Unhealthy DATA – :[85,78,265,284,1],
Note:
Posting the notification from API with help of OAuth 2.0 and Generate the access token will be covered in my next block.
8.4 Failure mode analytics Model management:
To analyze the notifications and failure modes for your equipment and model, you need to configure, train, and score the models in the “failure mode analytics” tile.
Procedure:
8.4.1 Unsupervised Topic Modeling Using Latent Dirichlet Allocation (LDA):
The unsupervised model identifies the characteristics of notification texts and maps the notification texts to the characteristics found in standard failure modes. After the training, it suggests the most appropriate failure mode for each notification.
You can perform validation tasks to validate and improve the suggestion.
The supervised model learns from this suggestion by performing text classification. This means, it learns the characteristics of individual failure modes from the mapped notification texts for upcoming notifications during the training. After the scoring, it maps the most appropriate failure modes to upcoming notifications.
8.4.2 Failure Mode Analytics with Supervised TextClassEnsemble [TEC]:
Our next step is feed the unsupervised model as an input to this model.
This algorithm conducts automatic supervised classification on text data using ensemble agreement between multiple classification algorithms that makes a prediction.
8.4.3 Train and Score the failure mode analytics model:
At least one failure mode analytics model has been configured for Train the model.
The notifications for your equipment meet the following three requirements:
If these requirements are not met, then the notifications will not be collected during the training.
PDMS portal is not supporting to create all the required parameter for the notification.
8.5 Failure mode analytics validation:
To improve the accuracy of the text analysis that maps topics with top words from notification texts to the most appropriate failure modes, sap recommend us to perform validation tasks. Validation tasks are generated based on a trained unsupervised model and are displayed on the failure model analytics validation application. Once you have performed a validation task, you can apply your validation to the next supervised model
Choose the right failure mode for each notification and click next button.
Then activate the model.
9 Failure mode analytics:
It uses the machine learning and it provides you with insights and analytics about your equipment and models with the last occurring failures. It uses unsupervised and supervised machine learning to extract topics with top words from notification texts. It also uses various metrics and visualizations to provide you with insights
you can find top words found within the notifications for the chosen failure mode and equipment model by relevancy in a bar graph and a list of all related notifications.
10 FAILURE PREDICTION and CONCLUSION:
So here is calculation – reliability engineer will do for predict the failure.
So the reliability engineer will notify / alert the technician to perform the required maintenance activity.
And also he will notify the suppliers / operator to keep the spare parts ready for this upcoming outlet temperature failure breakdown.