MAS AI/ML Modernization Accelerator: Air Compressor Use Case
By Carolyn Saplicki, IBM Data Scientist
Industries are constantly seeking innovative solutions to maximize efficiency, minimize downtime, and reduce costs. One groundbreaking technology that has emerged as a game-changer is asset performance management (APM) artificial intelligence (AI). However, embarking on the journey of implementing artificial intelligence (AI) in your asset performance management strategy can be both exciting and daunting. While the benefits of AI are widely recognized, getting started in this field comes with its own set of challenges.
Enter IBM Maximo® Application Suite (MAS), a powerful set of applications used for asset monitoring, management, predictive maintenance, and reliability planning. MAS enables companies to anticipate equipment failures, mitigate risks, and optimize their maintenance processes. Many businesses are in different stages of their MAS AI/ML modernization journey. Recognizing this, we have created “on-ramps”, designed to simplify the process of integrating AI into maintenance operations on MAS. This solution can be found on RedHat Marketplace.
In this blog, we delve into 4 different “on-ramps” we created in a MAS Accelerator to offer a straightforward path to harnessing the power of AI in MAS, wherever you may be on your MAS AI/ML modernization journey. These “on-ramps” are exploring ML/AI capabilities by leveraging out of the box models, migrating third-party models (custom), migrating third-party models (SROM-enhanced) and integrating third-party models. Each of these accelerators leverages state-of-the-art algorithms and machine learning techniques to identify anomalies accurately and in real-time.
Background
Our “on-ramps” accelerator focuses on industrial air compressors which are often considered the workhorse of a manufacturing plant. Industrial-grade air compressors are designed to provide a steady flow of compressed air for long periods of time and can take fluctuating surges in use typical in major manufacturing plants.
Air compressors are used for various purposes, such as powering pneumatic tools, providing compressed air for industrial processes, and supplying air for HVAC systems. Industries that use air compressors include manufacturing, automotive, construction, and energy.
The frameworks for each of these “on-ramps” can be utilized for other assets. All data scientists could leverage our patterns during an engagement. These patterns ensure consistency, efficiency, and collaboration among data science teams, making the MAS AI/ML modernization process smoother and scalable. We are leveraging Air Compressors data, but the solutions are generalizable. This means they can be applied to various industries and use cases beyond Air Compressors.
Maximo Application Suite
IBM Maximo® Application Suite (MAS) is a comprehensive application suite designed to empower businesses with efficient asset management and maintenance capabilities. With Maximo, businesses can effectively manage the entire lifecycle of their assets, from planning and procurement to maintenance and retirement. With its flexible architecture, scalability, and industry-specific solutions, MAS empowers businesses across sectors to achieve improved operational efficiency, cost savings, and enhanced asset reliability.
MAS AI/ML Modernization Accelerator “On-Ramps”
We have created a total of 4 different “on-ramps”:
Solution 1: Explore AI/ML in MAS
This data science solution predicts failure date, failure probability and anomalies in your assets. These problems can be solved by leveraging the out of the box (OOTB) solutions provided by MAS.
Solution 2: Migrate 3rd party models to MAS (Custom Model)
This data science solution predicts anomalies in air compressor assets using an isolation forest model. An isolation forest model is an unsupervised machine learning algorithm used for anomaly detection. It isolates anomalies by randomly partitioning the data into subsets. The goal is to use air compressor features in the isolation forest model to detect when an anomaly occurs.
Solution 3: Migrate 3rd party models to MAS (SROM-Enhanced Model)
This data science solution predicts anomalies in air compressor assets using all variables through the Smarter Resource and Operations Management (SROM) toolkit. The SROM toolkit that is supplied with Maximo Production Optimization On-Premises provides a consistent programming model, a scalable execution model for model training, hyperparameter tuning (HPT) and the solution deployment, and the industry-specific templates and tooling for frequently occurring use cases.
Using SROM, we create multiple models and pick the best performing one to be deployed, which in this case is a One Class SVM model. Isolation Forest, Nearest Neighbor Anomaly Model, Anomaly Robust PCA, Neural Network NSA, Random Partition Forest, Gaussian Graphical Model are some of the other models that are considered by SROM pipeline to pick the best performing model. In this use case, we used the unsupervised machine learning approach to detect anomalies in the sensors data. This means that there is no need to have labelled data from your client.
Solution 4: Integrate 3rd party models with MAS
This data science solution predicts anomalies in air compressor assets using an external model. These models can be deployed on client’s on-prem or any cloud platform. Through Watson Studio, we create a Python wrapper function to get results from the deployed models and integrate the model within Watson Machine Learning.
Accelerator Steps
Each of our “on-ramps” consists of three key steps designed to help businesses thrive in their AI/ML modernization journey:
- MAS Data Preparation
- MAS Asset/Device Registration
- Model Creation & Connection
Step 1: MAS Data Preparation
The first step of this solution ingests the pre-processed data into MAS Monitor via the Watson IoT Platform, using a Python library called pmlib. In our scenario, the data is stored in the Cloud Object Storage in Watson Studio. However, in a real use case you could receive this data from third party DBs which could be connected directly to IoT Platform. Once, the sensors data is stored in the Data Lake (DB2Warehouse), this information is then passed on to MAS Manage in step 2 using the same pmlib library.
Step 2: MAS Asset/Device Registration
Step 2 is crucial to store information on failure history and installation dates etc. The main difference between MAS Monitor and MAS Manage is their function: MAS Monitor oversees the management of operational data (OT), instead MAS Manage will collect organization’s information (IT), making data available to business applications and users of those apps. While IT systems are the repositories and processors of data, their OT counterparts are responsible for generating the information processed by IT.
Once devices/assets are registered, organizations can leverage MAS’s features for tracking asset location, managing maintenance schedules, recording work history, monitoring asset health, and generating reports and analytics.
Step 3: Model Creation & Connection
Finally, the third step, “Model Creation & Connection,” focuses on creating an anomaly detection model and connected it to MAS.
- Solution 1: The final step leverages both Monitor and Manage data to create OOTB predict models (for this scenario we will only focus on Estimated time to failure, Failure probability and Anomaly detection) in Predict. The pipelines provided by pmlib have multiple parameters that can take values to suit your data and problem. These OOTB models are computed in Predict and subsequently deployed in Monitor. Thanks to the scheduler, the results can be displayed in both Health UI and Monitor UI.
- Solution 2–3: In these solutions, we create and deploy a model or wrapper function in Watson Machine Learning. Once it is deployed, it is time to connect it to MAS. For this phase we need to prepare a wrapper function that through API calls score the model that we just deployed. This new wrapper function is then registered, deployed, and scheduled in Monitor.
Final Solution
No matter where you are in your AI/ML modernization journey, using the accelerator, you will ultimately get these results in your MAS Health and Predict platform. By harnessing the power of the MAS, you will witness the manifestation of your models, offering invaluable insights into anomaly detection. These tools will empower reliability engineers to promptly identify when assets, such as air compressors, require intervention and repairs.
It’s important to note that while this example revolves around air compressors, the versatility of these notebooks extends to various other assets as well.
Conclusion
Regardless of where your organization is with respect to your MAS AI/ML modernization journey, the adoption and integration of AI-driven predictive maintenance is a game-changer, proactively addressing maintenance needs, minimizing downtime, and optimizing operational efficiency. However, the complexities associated with AI implementation can sometimes pose challenges.
That’s where our comprehensive MAS Accelerator comes into play. Whether you are just starting out, looking to repurpose existing AI models, or seeking to optimize your current AI infrastructure, our solution provides the “on-ramps” you need to accelerate your time to market.
With IBM Cloud Pak® for Data, creating new AI models for predictive maintenance becomes an intuitive and efficient process. We simplify the model development journey, allowing you to customize AI algorithms to suit your specific use case.
Furthermore, our solution enables you to capitalize on the value of your existing AI investments. By repurposing and fine-tuning previous AI models, you can quickly apply them to predictive maintenance tasks without starting from scratch.
No matter where you find yourself on the path of APM modernization, our solution provides the tools, expertise, and support you need to accelerate your time to market. With our comprehensive approach, you can harness the transformative power of AI-driven predictive maintenance, unlock the full potential of your data, and drive operational excellence in today’s rapidly changing business landscape. Contact IBM Technology Expert Labs today.
