Taking Predictive Maintenance in Manufacturing to the Next Level! Optimal Sensor Selection and Operation Methods Supporting Stable Operation

In modern manufacturing, stable equipment operation is a critical factor determining corporate competitiveness. Implementing “predictive maintenance” is essential to avoid unexpected failure risks and maintain planned operation.
At the heart of implementing this predictive maintenance are various sensors. Sensor technology has evolved remarkably in recent years, enabling the capture of minute changes and the collection of extensive data. The real-time data gathered by these sensors serves as a vital source for objectively assessing equipment health.
Here, we explore how sensor monitoring is advancing predictive maintenance, the importance of selecting sensors that are suited to their application, and provide tips for maximizing their performance.

Practical Predictive Maintenance Evolving Through Sensor Monitoring

Sensor-based monitoring transforms predictive maintenance into a more practical and effective practice. Utilizing real-time data enables the early identification of potential equipment issues and facilitates the implementation of planned countermeasures.

Early Anomaly Detection Through Real-Time Monitoring

In predictive maintenance, sensors serve as the “eyes” that capture equipment status as numerical data. For example, rotating machinery generates minute vibrations, and their patterns serve as critical indicators of regular operation. High-precision vibration sensors detect such subtle changes and immediately relay signs of anomalies to the system in real time.
Additionally, temperature sensors monitor equipment for overheating or cooling abnormalities, while current sensors detect motor load anomalies.
Based on this data, the system issues alerts when values exceed set thresholds or when abnormal patterns are detected. This enables the identification of problems before failures occur, thereby minimizing unplanned equipment downtime.

Improved Predictive Accuracy Through Continuous Data Collection

Continuous data collection via sensors enables long-term monitoring of equipment “health.” This facilitates trend analysis of the data, allowing for accurate prediction of equipment degradation trends and the progression of wear.
For example, a gradual increase in vibration levels may indicate that the lifespan of a specific component is nearing its end. While traditional simple diagnostics can detect deviations from normal conditions through vibration data trend management, accumulated data can be combined with advanced technologies, such as AI (artificial intelligence) and machine learning, to enable more sophisticated analysis. These technologies extract failure precursors from complex patterns difficult for humans to identify, dramatically improving predictive maintenance accuracy. As a result, maintenance can be performed at more precise times, reducing unnecessary maintenance work while contributing to maximizing equipment uptime.

The Importance of Selecting the Right Predictive Maintenance Sensor for the Application

To achieve effective predictive maintenance, selecting the optimal sensor for the characteristics of the monitored equipment and the desired objectives is essential.
Choosing the correct sensor has a significant impact on the overall accuracy and effectiveness of the predictive maintenance system.

Types of Sensors and the Significance of Proper Selection

Sensors used for predictive maintenance include a wide variety, such as vibration, temperature, and current sensors. Each sensor measures a different physical quantity and is specifically designed for detecting abnormalities in a particular type of equipment.
For example, vibration sensors with a low-frequency band of 1 kHz or less are effective for detecting play or imbalance in rotating machinery, as specified in ISO 10816. While it has long been a technical convention that high-frequency vibration sensors (1 kHz to 20 kHz) are effective for early detection of bearing damage or gear wear in rotating machinery, the latest patented under-sampling technology now enables bearing damage detection using low-frequency vibration sensors.
Temperature sensors are optimal for capturing signs of motor overheating or lubrication issues. Furthermore, current sensors and insulation resistance meters excel at monitoring motor load fluctuations and insulation degradation.
These sensors differ in characteristics such as their measurement range, response speed, and durability. Selecting the optimal sensor based on the type of equipment being monitored, the operating environment (including high temperatures and humidity, dust, etc.), and the specific abnormalities you wish to detect is critically essential.
Selecting the wrong sensor increases the risk of failing to obtain necessary data or overlooking abnormalities, even with an expensive system in place. Proper sensor selection lays the foundation for a predictive maintenance system and significantly impacts the accuracy of subsequent data analysis and fault prediction.

Key Selection Points for Communication Methods and Deployment Environments

When selecting sensors, the method of data acquisition, particularly the communication method, is also crucial. In large-scale factories or environments where equipment is widely dispersed, traditional wired sensors often pose challenges in terms of wiring costs and labor. Therefore, sensors supporting wireless communication are recommended. Wireless sensors offer high installation flexibility, ranging from types that utilize existing infrastructure (such as Wi-Fi) to those capable of long-distance communication.

Choosing the appropriate communication method not only enhances data collection efficiency but also directly contributes to optimizing implementation costs. For example, Wi-Fi-enabled sensors that can utilize existing networks reduce the expense of building new communication infrastructure. Long-range transmission via Wi-Fi Halo has also become feasible. Conversely, specific environments may require more robust communication.
Thus, selecting a communication method suited to the installation environment and operational processes, alongside the sensor's measurement capabilities, is a crucial factor for effectively functioning predictive maintenance systems.

Tips for Maximizing the Performance of Predictive Maintenance Sensor Operations

Predictive maintenance sensors are not simply installed and forgotten; proper operation is essential to maximize their performance and enhance the system's effectiveness.

Optimizing Sensor Placement and Environmental Considerations

Selecting the optimal installation location is vital for sensors to demonstrate their actual value. For example, placing vibration sensors near areas prone to abnormalities makes it easier to detect subtle changes. Ensuring sensors are securely fixed to equipment and installed correctly also significantly impacts accuracy.

Additionally, environmental factors within the factory must be considered. Measures are required to minimize external noise, such as noise from other machinery or electromagnetic noise, which can affect sensor measurement accuracy. Creating an environment where sensors can perform at their best through appropriate shielding and thoughtful placement forms the foundation for reliable data acquisition.

Utilizing Acquired Data and Integrating with Diagnostic Systems

Utilizing Acquired Data and Integrating with Diagnostic Systems
The key to predictive maintenance lies not just in accumulating data collected from sensors, but in how effectively it is utilized. Data is typically visualized in a dedicated diagnostic system for precise, intuitive understanding. This allows operators to quickly grasp the equipment's operating status, trends, and anomaly histories at a glance.
Furthermore, alert settings that automatically notify responsible personnel when signs of abnormalities are detected are essential for enabling rapid initial response and preventing the escalation to major failures.
Selecting a diagnostic system with an intuitive interface that enables personnel without specialized knowledge to easily understand the system's diagnostic results and recommendations is crucial. By having the system automatically estimate abnormal areas and failure modes, the overall efficiency of maintenance operations can be improved.

NSXe provides solutions that powerfully support your predictive maintenance!

NSXe offers the Wi-Fi vibration sensor “conanair,” a proprietary product developed based on field feedback.
conanair primarily measures vibration. While its primary purpose is simple diagnostics to clarify the extent of equipment degradation, its patented technology also enables precision diagnostics to detect abnormalities in rotating machinery, such as bearings, and identify the causes of failure. Its purpose is to facilitate “predictive and preventive maintenance,” enabling planned maintenance work to understand the deterioration state of equipment and machinery and prevent sudden stoppages. This helps determine “what's different” by measuring relative vibration levels, allowing quantitative assessment of machine condition without relying on intuition. It can also pinpoint the cause of abnormalities.
For more detailed information, please do not hesitate to contact us.