Predictive maintenance in manufacturing with IIoT sensors is a data-driven approach that leverages industrial sensors, real-time data analytics, and machine learning to predict equipment failures before they occur. With continuous monitoring of health indicators like vibration, temperature, pressure, and energy consumption, IIoT sensors collect operational data from machinery. The data is then streamed to a centralized platform to analyze patterns and detect anomalies signaling signs of wear and faults.
The advantage of this approach significantly reduces downtime compared to traditional reactive or scheduled maintenance. Equipment is maintained when needed based on condition insights rather than fixed intervals or when the machine breaks down unexpectedly. Predictive maintenance helps reduce costs and saves on unnecessary repairs and minimizes production halts. With extended equipment lifespan and addressing problems early, it helps operational efficiency and overall equipment effectiveness.
Implementing predictive maintenance systems involves deploying smart sensors on equipment using edge computing for real anomaly detection. IIoT leverages cloud-based analytics and machine learning models for failure predictions. Visualization tools help maintenance teams monitor trends and schedule maintenance during a planned window. This approach transforms maintenance from a reactive to a strategic activity enhancing technical efficiency, safety, regulatory compliance, and asset utilization.
Best Sensors for Monitoring Bearings and Motors in Factories
Sensors are the key components in an IIoT device for predictive maintenance. Various sensors are used based on the machinery to detect vibrations, temperatures, and current. Some of the most common sensors include:
- Vibration Sensors:
Vibration sensors are the most common choice for detecting bearing and motor faults. Accelerometers are widely used to measure vibration signatures. Readings from vibration sensors reveal early signs of bearing wear, misalignment, imbalance, and motor issues. Wireless smart vibration sensors enable real-time remote monitoring with minimal installation complexities. Vibration sensors are often the first to indicate faults in machinery. When readings fluctuate, the sensors can send alerts when configured, varying for each machine. - Temperature Sensors:
Overheating can happen due to lubrication issues or friction increase. Monitoring bearing and motor temperatures with temperature sensors helps resolve issues before they become an accident or a total shutdown. Infrared thermography or embedded sensors provide quantitative data on temperature fluctuations indicating faults. Some sensors come with a combination of vibration and temperature detection in one device for comprehensive insight. - Current Sensors:
Stator current monitoring is a non-invasive method to monitor changes in current signatures, bearing faults, motor stalls, and electrical anomalies in electric motors. This approach allows you to monitor the condition of your motor without additional hardware in some cases. - Sensor Bearings:
Integrated sensor bearing units embedded with sensors directly in the bearing housing provide precise condition data in a compact form factor. The simplified installation and maintenance of sensor bearings make it a perfect sensor for motors requiring monitoring.
Wireless smart sensors are easy to deploy for data integration into IIoT platforms for real-time asset health management, preventing failures and optimizing maintenance schedules.
Implementation Costs and ROI for PdM (Predictive Maintenance) Projects
It’s impossible to estimate the cost of a predictive maintenance project as they vary widely depending on scale, existing infrastructure, and complexity. Some of the most general factors that influence cost are:
- Sensor hardware and installation on equipment for data collection:
This includes all the sensors required to monitor your machinery. The more complex and detailed the information you require, the number of sensors and the types of sensors are all factors that influence cost. - Data communication networks and edge computing devices:
While the sensors are an integral part of the predictive maintenance (PdM) model, the entire data analysis requires complex systems. This includes data communication networks and high-end edge computing devices, which often are high-priced. - Apart from data communication networks and computing devices, data storage is an integral part of predictive maintenance. For larger systems across various locations, cloud storage is preferred. Smaller single manufacturing units often require on-premise storage.
- Predictive analytics and machine learning models:
Machine learning is evolving, but each machinery is different, and they serve different purposes. There is no one-size-fits-all when it comes to machine learning. Machine learning models are new but are evolving fast. Creating a machine learning model with predictive analytics is usually a tailored process crafted for your factory or machines. This adds up to the cost. - Integration with existing systems:
This involves creating custom workflows or integrating the system into the existing workflow without interrupting the whole process. If you have an ERP system or other enterprise systems, the predictive maintenance model data is integrated into your system. - Training and maintenance:
The last process is to help your employees and staff understand the new system, the process of how to acquire data, and how to analyze it. The training may span anywhere between a few hours to weeks depending on the complexity of the system.
Predictive maintenance models are complex and have various other gears that influence the overall process. This includes security, data storage and management, and more. We encourage you to read more on IIoT devices and predictive maintenance using industrial IoT or contact us for more information on the services we provide.