Introduction
Edge vs Cloud Computing: Finding the Best Fit for Industry
As industries turn to smart technologies to keep machines running efficiently, a major debate has emerged: Should businesses rely on edge vs cloud computing for condition monitoring?
Both computing models have strengths and weaknesses. Cloud computing is great for storing and processing large amounts of data, but it depends on stable networks and can introduce latency issues. Edge computing, on the other hand, processes data locally, ensuring real-time fault detection, but its limited computational power can be a drawback.
For condition monitoring of industrial machines, picking the right approach is crucial. This blog explores the practical differences and real-world applications of edge vs cloud, helping businesses make informed decisions about their computing needs.
Understanding Condition Monitoring in Industrial Motors
The Shift from Reactive to Predictive Maintenance
Industries used to rely on scheduled maintenance and manual inspections to monitor machine health. This approach often led to unexpected breakdowns, causing costly downtime and repairs.
With the rise of data-driven condition monitoring (DDCM), companies now use real-time machine learning models to predict failures before they occur. This shift allows businesses to fix problems early, improve operational efficiency, and reduce costs.
How Machine Learning Helps Detect Faults Early
Machine learning has revolutionized fault detection in industrial motors, replacing guesswork with data-driven insights. By analyzing current, voltage, vibration, and temperature signals, ML models identify early signs of wear and tear before they cause serious failures.
Popular machine learning models used in condition monitoring include:
| Algorithm | Strengths | Best Use Cases |
|---|---|---|
| Support Vector Machines (SVM) | Great for identifying complex patterns | Fault detection using sensor signals |
| K-Nearest Neighbors (KNN) | Adapts well to varied datasets | Recognizing motor health conditions |
| Decision Trees (DT) | Simple and fast | Diagnosing rotor bar faults |
These models allow industries to detect and classify faults early, minimizing downtime and improving reliability.
Challenges in Industrial Condition Monitoring
Despite its benefits, implementing machine learning-based condition monitoring comes with a few challenges:
- Latency Issues: Cloud computing can introduce delays in real-time fault detection, making quick responses difficult.
- Resource Limitations: Edge computing is fast but has limited power, restricting its ability to process complex data.
- Network Burden: Transmitting raw sensor data to the cloud requires high bandwidth, increasing operational costs.
Understanding these challenges helps industries decide whether edge computing for instant detection or cloud computing for large-scale analysis is the better choice.
Edge vs Cloud: Choosing the Right Approach for Condition Monitoring
When it comes to keeping industrial machines running smoothly, businesses are turning to smart technologies for predictive maintenance. One of the biggest decisions they face is choosing between edge computing and cloud computing.
Each approach comes with its own strengths and weaknesses. Edge computing processes data locally, providing real-time fault detection, while cloud computing offers high computational power for analyzing large datasets. But which one is right for your industrial setup? Let’s break down the key differences and see how each performs in condition monitoring applications.
Edge Computing: Fast, Localized Processing for Immediate Fault Detection
How Edge Computing Works in Industrial Monitoring
Edge computing processes data at the source—meaning sensor data from industrial machines is analyzed on-site rather than being sent to a remote cloud server. This ensures real-time fault detection, allowing companies to act immediately when issues arise.
Devices like Raspberry Pi, commonly used in industrial environments, enable machine learning models to run directly at the edge, flagging potential faults before they turn into bigger problems.
Why Edge Computing Is Ideal for Real-Time Monitoring
| Benefit | How It Helps Condition Monitoring |
|---|---|
| Low Latency | Immediate fault detection with no network delays. |
| Reduced Network Burden | Less reliance on data transmission to cloud servers. |
| Improved Data Privacy | Keeps sensitive industrial data on-site. |
| Lower Operational Costs | No ongoing cloud subscription fees—just a one-time device cost. |
Limitations of Edge Computing
While edge computing is fast and efficient, it does have its limitations:
- Limited computational power, making it harder to run complex machine learning models.
- Scalability issues, especially when handling large datasets.
- Frequent model updates can be difficult due to hardware constraints.
Edge computing is great for instant fault detection, but may struggle with big data analysis, making cloud computing a better option for long-term monitoring and deep learning applications.
Cloud Computing: Powerful Data Analysis for Large-Scale Monitoring
How Cloud Computing Supports Condition Monitoring
Cloud computing uses remote servers to process, store, and analyze industrial data. Platforms like Amazon Web Services (AWS) EC2 allow businesses to run advanced machine learning models, offering deep analytics and insights into equipment health.
Cloud computing is not restricted by hardware limitations, meaning it can handle massive amounts of sensor data, making it perfect for long-term predictive maintenance.
Why Cloud Computing Is Best for Large-Scale Industrial Monitoring
| Benefit | How It Helps Condition Monitoring |
|---|---|
| Scalability | Easily handles large volumes of data from multiple machines. |
| Advanced Analytics | Supports complex AI models for predictive maintenance. |
| Long-Term Data Storage | Keeps detailed historical records for future analysis. |
| Frequent Model Updates | Allows easy retraining and fine-tuning of machine learning models. |
Limitations of Cloud Computing
Despite its advantages, cloud computing has a few downsides:
- Higher costs—cloud services charge fees based on usage.
- Network dependency—requires strong internet connections for real-time updates.
- Security concerns—data is stored externally, increasing cybersecurity risks.
While cloud computing offers strong computational power, businesses need to consider whether its costs and network dependency align with their operational needs.
Trade-Offs Between Edge and Cloud Computing
Choosing between edge vs cloud computing comes down to balancing speed, scalability, cost, and network reliability.
Key Comparisons Between Edge and Cloud Computing
| Factor | Edge Computing | Cloud Computing |
|---|---|---|
| Latency | Extremely low—instant processing | Higher—data transmission delays |
| Cost | One-time hardware cost | Recurring service fees |
| Scalability | Limited—best for smaller data sets | High—handles big data and complex models |
| Data Privacy | Local processing—better security | External storage—higher security concerns |
| Network Dependency | Works without an internet connection | Requires strong network stability |
| Computational Power | Limited by device capacity | Powerful—supports deep learning models |
If real-time fault detection is your priority, edge computing is the way to go. If your business requires large-scale data processing and predictive maintenance, then cloud computing is the better option.
The Impact of Resource Constraints and Network Variability
How Resource Constraints Affect Edge vs Cloud
Computational power is one of the biggest differences between edge and cloud computing.
- Cloud platforms like AWS can handle intensive AI models, while edge devices like Raspberry Pi struggle with high-resource applications.
- Edge computing works best for lightweight machine learning models, while cloud computing excels with deep learning applications.
Network Variability: A Crucial Consideration for Cloud Computing
Industries relying on cloud-based condition monitoring must account for network stability.
- Poor network connections can delay fault detection, impacting equipment performance.
- Edge computing eliminates network dependency, making it the best option for remote locations or areas with unreliable internet.
If your business operates in network-constrained environments, edge computing ensures uninterrupted monitoring without relying on an internet connection. However, if high-speed internet is available, cloud computing offers advanced analytics and greater scalability.
Final Thoughts: Which Computing Approach Is Right for You?
Picking between edge vs cloud computing depends on your industry needs:
- Use Edge Computing when:
- Real-time responses are critical.
- Network connections are unreliable.
- Immediate fault detection and localized processing are required.
- Use Cloud Computing when:
- Large-scale data analysis and deep learning models are needed.
- Long-term condition monitoring and historical tracking matter.
- Predictive maintenance strategies depend on big data processing.
For industries seeking the best of both worlds, a hybrid approach can balance real-time fault detection at the edge with advanced analytics in the cloud.
Regardless of the choice, edge and cloud computing will continue to transform industrial condition monitoring, allowing businesses to reduce downtime, optimize maintenance, and improve equipment reliability.
How Edge and Cloud Computing Perform in Real-World Industrial Applications
As industries move toward data-driven condition monitoring, one of the biggest decisions they face is choosing the right computing model. Should businesses use edge computing, which processes data locally for real-time fault detection, or should they rely on cloud computing, which provides powerful computing resources for large-scale analytics?
This study explores both approaches, comparing Raspberry Pi (edge) vs AWS EC2 (cloud) to determine which performs better in fault detection for induction motors. The results offer valuable insights for industries looking to optimize their predictive maintenance strategies.
Testing Edge vs Cloud: Raspberry Pi vs AWS EC2
Why These Platforms Were Selected
To evaluate the performance of edge vs cloud computing in condition monitoring, researchers tested two widely used computing platforms:
- Raspberry Pi 3B (Edge): A low-cost device capable of on-site data processing with minimal network dependency.
- AWS EC2 (Cloud): A high-performance cloud platform built for scalability, large-scale data analysis, and machine learning workloads.
The study examined four key performance metrics to compare these platforms:
- Computational Speed – How fast each platform processes machine learning models.
- Resource Consumption – CPU and memory usage during training and inference.
- Scalability – How well each platform handles increasing data sizes.
- Cost Efficiency – The financial impact of edge vs cloud deployment.
How Machine Learning Models Perform in Fault Detection
Which Algorithms Were Tested?
Three common machine learning models used in industrial monitoring were evaluated on both edge and cloud computing platforms:
| Machine Learning Model | How It Helps Condition Monitoring |
|---|---|
| Support Vector Machines (SVM) | Detects complex fault patterns in sensor data. |
| K-Nearest Neighbors (KNN) | Classifies motor conditions based on proximity in the feature space. |
| Decision Trees (DT) | Simple and fast—helps classify rotor faults efficiently. |
These models were tested to identify broken rotor bars—a common fault in industrial motors that can cause significant downtime if left undetected.
Computational Speed: Training & Inference Times Compared
Speed is crucial when it comes to fault detection. The study measured how quickly edge vs cloud computing platforms trained and processed machine learning models.
| Model | Edge: Training Time (Seconds) | Cloud: Training Time (Seconds) | Edge: Inference Time (Seconds) | Cloud: Inference Time (Seconds) |
|---|---|---|---|---|
| SVM | 0.0094 | 0.0004 | 0.0031 | 0.0011 |
| KNN | 0.0043 | 0.0011 | 0.0207 | 0.0023 |
| DT | 0.0067 | 0.0012 | 0.0013 | 0.0006 |
- Cloud computing was over 800% faster in training times compared to edge computing.
- Inference times (real-time fault detection) were 10X quicker on the cloud, meaning faster responses.
- Edge computing performed well for immediate detection, but slowed down when processing larger datasets.
Resource Consumption: CPU & Memory Usage Compared
Computational efficiency is one of the biggest differences between edge and cloud computing. The study measured CPU and memory usage to understand how each platform handles machine learning workloads.
| Model | Edge: CPU Usage (%) | Cloud: CPU Usage (%) | Edge: Memory Usage (MB) | Cloud: Memory Usage (MB) |
|---|---|---|---|---|
| SVM | 8.96 | 0.17 | 75.70 | 13.30 |
| KNN | 7.22 | 0.28 | 76.60 | 13.40 |
| DT | 8.07 | 0.41 | 71.61 | 13.30 |
- Edge computing consumed nearly 3X more CPU power than cloud computing, making it less efficient for handling complex models.
- Memory usage on edge devices was significantly higher, further limiting scalability.
- Cloud computing had minimal CPU and memory load, allowing continuous monitoring without performance drops.
Scalability: Can Edge Computing Handle Large Datasets?
For businesses handling big data, scalability is critical. The study tested edge vs cloud computing using different dataset sizes to understand how well each platform scales.
| Data Size (%) | Edge: Inference Time (Seconds) | Edge: Accuracy (%) | Cloud: Inference Time (Seconds) | Cloud: Accuracy (%) |
|---|---|---|---|---|
| 10% | 0.0020 | 100.00 | 0.0000 | 100.00 |
| 50% | 0.0214 | 97.22 | 0.0000 | 97.22 |
| 100% | 0.0315 | 97.26 | 0.0000 | 97.26 |
- Edge computing struggled with larger datasets, increasing inference time significantly.
- Cloud computing maintained fast inference speeds, proving better suited for big data applications.
Cost Efficiency: Long-Term Financial Impact of Edge vs Cloud Computing
Deploying machine learning models for condition monitoring comes with financial implications. The study compared the costs of running models on edge vs cloud platforms.
| Cost Factor | Edge Computing (Raspberry Pi) | Cloud Computing (AWS EC2) |
|---|---|---|
| Hardware Purchase | $35 (One-time cost) | N/A (Subscription-based) |
| Operational Cost | Low | $8.14 (for 10 hours of use) |
| Scalability Cost | Fixed | Scales with usage |
| Data Transmission Cost | Minimal | High—network bandwidth required |
- Edge computing requires a small one-time investment, making it cost-efficient for static models.
- Cloud computing incurs recurring costs, but supports large-scale analytics and frequent model updates.
Real-World Case Study: Induction Motor Fault Diagnosis
How Fault Detection Works Using Machine Learning
This study focused on detecting broken rotor bars in induction motors using Motor Current Signature Analysis (MCSA).
- Data Used: Three-phase stator currents and vibration signals collected from industrial machines.
- Feature Engineering: Signals analyzed using Power Spectral Density (PSD) to extract fault indicators.
- Feature Selection: Principal Component Analysis (PCA) optimized dataset complexity while maintaining critical information.
Key Takeaways from the Case Study
- Edge computing detected faults in real-time, but struggled with large-scale processing.
- Cloud computing handled complex fault classification efficiently, making it better suited for big data analysis.
- Feature selection techniques helped optimize edge performance, reducing training times but increasing inference delays.
How Edge and Cloud Computing Perform in Real-World Industrial Applications
Industries rely on data-driven condition monitoring to ensure machines run efficiently, prevent downtime, and minimize costly failures. But when it comes to processing and analyzing sensor data, businesses must decide between edge computing, which processes data locally for real-time fault detection, or cloud computing, which offers high-powered analytics for large-scale monitoring.
This study compares Raspberry Pi (edge) vs AWS EC2 (cloud) to determine how each platform performs in fault detection for industrial motors.
Testing Edge vs Cloud: Raspberry Pi vs AWS EC2
Why These Platforms Were Selected
Researchers tested two widely used computing platforms for industrial fault detection.
- Raspberry Pi 3B (Edge): A low-cost device ideal for on-site processing with minimal reliance on network connections.
- AWS EC2 (Cloud): A powerful cloud service designed for scalable, high-performance computing and advanced machine learning workloads.
The study examined four key factors to compare these platforms:
- Computational Speed – How fast each system processes machine learning models.
- Resource Consumption – CPU and memory usage during model training and inference.
- Scalability – Whether each platform handles increasing data loads effectively.
- Cost Efficiency – The financial impact of using edge vs cloud computing.
How Machine Learning Models Perform in Fault Detection
Which Algorithms Were Tested?
Three machine learning models were used to detect faults in induction motors:
- Support Vector Machines (SVM): Identifies complex fault patterns in sensor data.
- K-Nearest Neighbors (KNN): Classifies motor health based on similarity in datasets.
- Decision Trees (DT): Fast and interpretable, making it useful for diagnosing rotor faults.
These models analyzed sensor signals from industrial motors to detect broken rotor bars, a common fault that can lead to serious failures.
Computational Speed: Training & Inference Times Compared
The study measured how quickly edge vs cloud computing platforms trained and processed machine learning models.
- Cloud computing trained models over 800% faster than edge computing.
- Inference times were up to 10X quicker on the cloud, meaning faster fault detection.
- Edge computing worked well for immediate responses but slowed down when handling large datasets.
For businesses that need quick fault identification, edge computing is useful. However, for complex analytics on large-scale data, cloud computing is the better choice.
Resource Consumption: CPU & Memory Usage Compared
When comparing CPU and memory usage, edge computing required significantly more resources than cloud computing.
- Edge computing consumed nearly three times more CPU power, making it less efficient for handling complex models.
- Memory usage was significantly higher on the edge, limiting scalability for larger datasets.
- Cloud computing had minimal CPU and memory load, allowing continuous monitoring without performance issues.
For companies that require constant, large-scale monitoring, cloud computing is more practical. Edge computing works well for specific, localized fault detection but has limitations when scaling up operations.
Scalability: Can Edge Computing Handle Large Datasets?
Scalability is crucial for industries handling big data. The study tested edge vs cloud computing by increasing dataset sizes.
- Edge computing slowed down as datasets grew, affecting real-time fault detection.
- Cloud computing maintained near-instant fault detection regardless of data volume.
While edge computing is ideal for immediate responses, it struggles when managing large datasets over time. Businesses with expanding operations will benefit more from cloud computing’s scalability and computational efficiency.
Cost Efficiency: Financial Impact of Edge vs Cloud Computing
The study compared upfront and operational costs of edge vs cloud computing.
- Edge computing required a one-time hardware investment, making it cost-effective for basic monitoring.
- Cloud computing operates on a pay-as-you-go model, with costs increasing based on usage.
- AWS EC2 had recurring costs, but its computational power and scalability justified the investment.
Businesses that need real-time fault detection with minimal ongoing costs may prefer edge computing. Those requiring long-term monitoring and frequent model updates should consider cloud computing despite its recurring expenses.
Real-World Case Study: Induction Motor Fault Diagnosis
How Fault Detection Works Using Machine Learning
This study focused on detecting broken rotor bars in induction motors using Motor Current Signature Analysis (MCSA).
- Data Collected: Three-phase stator currents and vibration signals.
- Feature Engineering: Signals were analyzed using Power Spectral Density (PSD) to extract fault indicators.
- Feature Selection: Principal Component Analysis (PCA) optimized dataset complexity while maintaining key fault-related features.
Key Takeaways from the Case Study
- Edge computing successfully detected faults, but struggled with large-scale data processing.
- Cloud computing handled complex fault classification efficiently, making it better suited for big data analysis.
- Feature selection techniques helped optimize edge performance, reducing training times but increasing inference delays.
Conclusion
The Future of Edge vs Cloud in Industrial Condition Monitoring
As industries continue evolving toward smart maintenance strategies, the debate between edge vs cloud computing will shape the next generation of condition monitoring solutions. While edge computing excels in real-time fault detection, offering low-latency responses and reduced network burden, cloud computing provides unmatched computational power and scalability, making it ideal for large-scale data processing and predictive analytics.
Looking ahead, hybrid solutions that combine edge and cloud computing will dominate industrial environments. Edge devices will process critical sensor data locally, ensuring immediate fault detection, while cloud computing will provide long-term insights, deep learning capabilities, and large-scale analytics. This balanced approach will optimize performance, reduce downtime, and improve maintenance strategies across industries.
How AI-Driven Insights and Feature Selection Techniques Will Shape Next-Gen Strategies
Artificial intelligence is revolutionizing industrial condition monitoring, making fault detection more accurate and proactive. Machine learning models such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Decision Trees (DT) have already demonstrated impressive fault detection capabilities in induction motors, and future advancements will refine these models even further.
Feature selection techniques, such as Principal Component Analysis (PCA), help streamline fault detection models, improving computational efficiency and reducing data transmission costs. These techniques will be essential for edge computing, where resource constraints require optimized machine learning models to function efficiently. As AI continues to evolve, businesses will fine-tune their predictive maintenance strategies using AI-powered analytics, ensuring smarter decision-making and improved asset management.
Final Thoughts on Leveraging Data Science for Smarter Fault Detection
The future of condition monitoring will rely on data-driven insights, AI optimization, and strategic computing decisions. Whether industries choose edge computing for immediate detection or cloud computing for scalable analytics, the integration of AI and machine learning-driven condition monitoring will lead to smarter fault detection, reduced maintenance costs, and improved machine reliability.
By embracing data science and AI-driven condition monitoring, industries
Reference:
Walani, C.C., & Doorsamy, W. (2025). Edge vs Cloud: Empirical Insights into Data-Driven Condition Monitoring. Big Data and Cognitive Computing, 9(121). MDPI. https://doi.org/10.3390/bdcc9050121
License:
This article is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can access the license details here: https://creativecommons.org/licenses/by/4.0/