Machine Learning in Action: Discover How Ksolves Fixed Compressor Short Cycling In Refrigeration System?

Short Cycling is a common issue in refrigeration systems. It reduces the compressor lifespan and strains the systems by causing frequent on-off cycles. For businesses relying on refrigeration, this can lead to spiked energy bills with higher chances of compressor failures.

Recently, a client facing these exact setbacks turned to Ksolves. They needed an innovative solution to prevent short cycling and reduce operating costs. That’s when we created an intelligent AWS-powered ML solution. And now, it’s already delivering accurate forecasts and helping our client early detect & prevent compressor short cycling. 

Ready to discover how we did that? Let’s explore how we leveraged Machine Learning to make compressor maintenance more efficient and affordable!

Main Challenges Our Client Faced As A Refrigeration Service Provider:

When the client discovered our ML consulting services, we were excited to help them resolve their significant hurdles in maintaining refrigeration compressors. Here’s what they needed help with:

• They required a prominent data management strategy to handle large volumes of unstructured data and further processing.
• They lacked the domain expertise to distinguish between Normal and Short Cycling and analyze the compressor behavior patterns.
• They faced integration and dependency issues during CodeBuild pipeline deployment and were required to perform troubleshooting.
• They struggled to configure the ECS and Fargate clusters and align the setup with consistent network and security settings.

These challenges only cost our clients time, resources, and operational consistency. So, our ML engineers wanted to build a robust solution to tackle these challenges!

The Ksolves Approach: Building an Intelligent Solution Using Machine Learning

Our solution experts planned to custom-build a solution using advanced machine learning and Amazon Web Services for infrastructure. Here’s how we approached it:

• • Handling Massive Data Sets:  We followed a streamlined approach with a robust data management strategy to extract AMPS values accurately.
  • Accurate Short Cycle Identification: Our team leveraged our domain expertise to develop a specialized model that preemptively spots the subtle signals of short cycling.

• • ML Model Training: We created a  Machine Learning model and optimized it by tuning the hyperparameters for precise short-cycle predictions.

• • MLOps Orchestration using  AWS: We created dual code pipelines using AWS to automate model training and prediction workflows. 

Let’s move on to the next part, where we discuss our steps to predict and prevent this compressor short-cycling problem.

Steps Followed By Our Engineers To Solve Compressor Short Cycling

Now, let’s understand the functional capabilities of the Machine Learning-powered solution we developed. 

Data Extraction and Preparation 

First, we initiated an efficient data extraction process to capture AMPS values from unstructured sources. Next, we organized this data systematically by date and aligned it with its time of occurrence. We also employed noise reduction filters to maintain high data quality for accurate model predictions.

Machine Learning Model Selection & Implementation

After doing exploratory data analysis, we choose the XGBRegressor() class from the XGBoost library to train the ML model. Our model could precisely detect short cycles thanks to its ability to handle complex data patterns & robustness to outliers. We also customized the model with specific hyperparameters as arguments to fine-tune its performance accuracy.

Implementing AWS Automation For Scalability 

Our Machine Learning engineers developed dual pipelines using AWS CodePipeline and CodeBuild to ensure seamless system operation. These automated pipelines, crafted as part of our comprehensive ML consulting solutions, enabled continuous workflows for the client. This fully automated approach helped us accurately calculate the number of short cycles based on segment counts.

From Prediction to Prevention: Uncovering The Impact of Our Unique ML-Powered Solution

After reviewing the technical capabilities of Ksolves Machine Learning-powered intelligent solution, let’s rewind what problems got fixed:

• Extended Equipment Lifespan: Our solution predicts real-time short cycling occurrences. With our solution, manufacturing facilities can reduce the chances of compressor failures and save a lot on energy bills.

• Fewer Disruptions with More Stability: This intelligent solution minimizes system disruptions and sudden downtime. So, manufacturers can focus on their work priorities without worrying about unexpected breakdowns.
• Proactive Maintenance With Reduced Spending: The ML-driven solution goes beyond reactive repairs. It enables the manufacturers to plan proactive maintenance and upkeep based on data-driven insights.

The results from this advanced solution showcase the potential of data-driven intelligence. It’s ideal for addressing complex challenges most manufacturing businesses face using refrigeration systems. 

Explore Next-Gen ML Consulting Services By Ksolves

Ksolves tech solutions offer a proven approach that’s innovative and adaptable. It perfectly showcases how this technology can transform complex industry challenges into manageable data-driven solutions.

We bring targeted Machine Learning strategies to life to custom-develop pattern recognition, anomaly detection, or recommendation engines. Our highly skilled engineers specialize in creating future-ready ML solutions tailored that highlight the following business benefits:

• Tailored Proof of Concept: We always provide a proof of concept for every solution. It’s perfect for visualizing how our solution functions and what operational transformation it can bring!
• Data-Driven Precision: Our approach blends transparency with accuracy through a mix of black-box and white-box AI models. That’s why you get the highest quality outcomes from our Machine Learning development solutions.
• End-to-End Support: From data preparation and model deployment to ongoing support and maintenance, our services cover every stage to guarantee seamless integration and performance.

We are proud to say that we deliver top-notch ML consulting solutions that turn complex business data into manageable and profitable data strategies. Our expertise in Machine Learning development accelerates project timelines, reduces operational costs, and drives long-term growth. 

Interested in discovering how ML-driven data insights could help your business? We’re here to hear!

Read our case study – Compressor Short Cycling Detection Using Machine Learning