Custom-made Machine Condition Monitoring Software using Microsoft Azure
Every machine is doomed to fail! That's the thought that keeps maintenance engineers up at night. And rightly so. Machine failures cause insurmountable losses to the company in terms of time, money, and even reputation. Studies suggest that equipment downtime hampers productivity by anywhere between 5% to 20% for an average factory. Hence, monitoring equipment is essential for transitioning from reactive firefighting to predictive planning.
Let’s understand how machine condition monitoring software, built with expert machine learning consulting services, can transform the value proposition for smart machine manufacturers
Why machine condition monitoring is necessary?
Inefficient maintenance is one of the prime contributors to unplanned downtime, and research suggests that the average manufacturer has to face 800 hours of downtime every year. The average downtime cost in the automotive industry is around $22,000 per minute. Every manufacturing operation is challenged by forces of nature: heat, pressure, moisture, vibrations, chemical reactions, etc. These forces can also have a compound effect based on the environment in which machines operate.
For example, at the customer's site, vibrations experienced by your machine can be different if they are close to other machines compared to testing environments. Temperature changes in your machine may fluctuate due to unpredictable fuel or air supply stoppage. Changing humidity levels can also affect the sensitivity of sensors. Although almost all companies are vulnerable to issues like these, only some are well-equipped to tackle them. Many companies still rely on traditional preventive maintenance methods, which involve periodic maintenance checks. Planned stoppages ensure the smooth functioning of machines, but they also contribute to hindrances in overall productivity.
What if you could know the remaining useful life of your machine from its current behavior? How about knowing the best time to schedule routine maintenance? How about knowing well in advance when the parts likely need replacement?
Insights like these make machine condition monitoring an essential part of any future-focused production line, as it identifies potential failures before they occur using machine learning For manufacturers, this translates to better planning and less costly repairs of equipment.
How expert-led machine learning consulting services help enhance predictive maintenance?
In a 2021 survey, the top 400 manufacturing companies reported having better control over their processes even during the pandemic. 56% of these companies attributed this success to adopting digital technologies that monitored machine operations and helped them respond effectively to crises.
The data that an intelligent machine condition monitoring system gathers is used to train algorithms and deploy solutions that can predict a machine's remaining useful life. This dramatically reduces downtime by making systems more intelligent about failures, availability, and operating efficiencies.
Machine learning consulting in action: Improving uptime with custom software
As an instructive example, consider how a UK-based instrumentation engineering company builds custom software to capture, manage and analyze data, for real time insights and take timely preventive measures, avoid costly downtime, and boost overall productivity.
Machine condition monitoring goes a step further by setting alarms based on severity and highlight critical red flags before issues escalate. By leveraging our machine learning models, these alerts become not only predictive but also highly actionable, reducing false positives and negatives and empowering manufacturers to act with confidence.
Intelligence-as-a-Service for Equipment Monitoring
Custom solutions enable manufacturers to integrate and streamline predictive maintenance efforts with their existing systems. Unlike off-the-shelf software, custom solutions are tailored to unique requirements, supporting diverse sensor protocols and enabling real-time dashboards for better decision-making.
For example, a test and measurement instruments manufacturing leader built a platform to capture data from multiple sources in real-time, orchestrate it, and deliver it via APIs, providing Intelligence-as-a-Service for equipment monitoring, designing predictive analytics pipelines and integration workflows.
Why custom-made machine condition monitoring software is critical?
- Integrating diverse sensors and protocols
Different machines utilize various sensors and protocols, which can create enterprise-level challenges if data is scattered across multiple systems. A custom solution integrates all data into unified dashboards, enabling faster and more efficient decision-making.
- Real-time data and unified dashboards
Custom solutions also provide flexibility to isolate, acquire, and analyze data at different endpoints, whether at the edge or cloud. Off-the-shelf products often limit connectivity beyond standard protocols. Setting baselines for key parameters enables real-time analytics and insights into predictive maintenance.
- Algorithm training, fine-tuning, and deployment
Algorithms are trained using historical data, fine-tuned, and deployed for predictive maintenance. Once models achieve satisfactory accuracy, they can be integrated with other systems, such as spares management or maintenance management.
- Integrating analytics with maintenance management systems
Predictive alerts generated through machine learning can be seamlessly integrated with operational workflows, enabling timely decision-making and reducing unplanned downtime.
Why Microsoft Azure is the preferred platform?
- Cloud-based real-time diagnostics
Microsoft Azure's IoT and ML tools provide high-fidelity vibration data acquisition, real-time diagnostics, and predictive analytics. Azure services, IoT Hub, Event Hub, Stream Analytics, Data Factory, Azure ML, Synapse, and Power BI, integrate diverse protocols and provide insights for predicting failures.
- Scalable and flexible architecture for manufacturing
Azure solutions are scalable and flexible for a single machine or an entire fleet running continuous shifts.
Step-by-step implementation for optimized machine performance
Identifying parameters and sensor integration
- Identify key parameters affecting machine condition: vibration, temperature, pressure, etc.
- Ensure appropriate sensors transmit data reliably.
- Decide between off-the-shelf or custom-built solutions.
Designing custom solutions
- For a custom solution, engage machine learning consulting services or an in-house expert team.
- Acquire and unify data from sensors, PLCs, OPC Servers, and gateways.
- Decide which data should reside on the edge and which on the cloud.
- Build a scalable architecture using Azure modules and services.
- Process data to generate alerts, notifications, KPIs, and dashboards.
Embracing the future: predictive maintenance and business success
Every machine may eventually fail, but downtime doesn't have to. By combining custom machine condition monitoring software with advanced analytics and predictive algorithms, manufacturers gain the ability to act proactively, reduce costly interruptions, and maintain consistent production performance. Predict failures, optimize maintenance schedules, and enhance operational efficiency.
Stay ahead, keeping machines running smoothly and operations efficient. The future belongs to those who anticipate challenges before they arise, rather than reacting to them.
Key Takeaways
- Predictive maintenance reduces downtime and operational costs.
- Custom software integrates seamlessly with diverse industrial machinery.
- Microsoft Azure provides a scalable, reliable, and flexible analytics infrastructure.
- Expert-led Machine learning consulting services ensure proactive, intelligent maintenance strategies.
- Businesses that embrace predictive insights are better positioned for growth and operational excellence.