In the dynamic landscape of industrial technology, where staying ahead is paramount, condition monitoring emerges as a pivotal force in safeguarding the health and performance of machinery.

This blog is crafted for industry professionals, engineers, and technology enthusiasts seeking a nuanced exploration of the latest developments in sensor technology, AI, and battery life and how these advancements sculpt the present and future landscape of condition monitoring within industrial settings.

Exploring Wireless Sensor Options

The journey into condition monitoring often begins with the question: Why aren't there more wireless sensor options for common industrial indicators beyond vibration? While wireless sensors for vibration have experienced a boom, thanks to improvements in protocols like BLE and the advent of MEMS accelerometers, the same cannot be said for other critical parameters for oil analysis.

The challenge lies in the core sensor transducer technologies. While breakthroughs for indicators like the presence of particles, moisture, acidity, TAN/TBN, viscosity, etc., may exist in university labs, available oil condition-monitoring sensors present a different story. These sensors, designed to check the health of lubricants, often come in large enclosures powered by industrial-level supplies, which are not conducive to wireless transmission.

IIoT vendors are introducing sensor 'hub' topologies in response to this challenge. These hubs serve as wireless bridges that accept analog inputs from traditional industrial sensors and digitize their outputs. This innovation broadens the sensors for condition monitoring systems, enabling a more comprehensive approach to machine health.

IIoT hubs can digitize signals related to oil temperature, humidity, pressure, temperature probes, ultrasound, current, and machine rotation. Once digitized, the data from these sensors can integrate with the same network used for vibration sensors, providing a holistic view of the machinery's health. Additionally, IIoT vendors are expanding their offerings to include tools for lubrication maintenance, further enriching the condition-monitoring toolbox.

Despite these advancements, the blog highlights that the analysis of oil samples is likely to remain in the domain of qualified experts. However, integrating sensors and IIoT tools aims to enhance efficiency, mirroring the current state of vibration condition monitoring.

Unleashing Advanced Capabilities for Condition Monitoring

Artificial intelligence has become a ubiquitous term, permeating discussions across websites and social media platforms. In industrial maintenance, several IIoT vendors have entered the condition-monitoring market, touting the AI capabilities of their vibration-sensing systems. The blog delves into the essence of AI in this domain, examining whether it has lived up to the game-changing expectations.

The capabilities described by vendors go beyond classical vibration signal processing. Data-driven approaches, a hallmark of AI, involve applying basic statistics to vibration waveforms to create many condition indicators, each sensitive to specific aspects of machine behavior. This departure from traditional methods is crucial for a more nuanced understanding of machinery health.

Moreover, data engineering plays a pivotal role in AI-driven predictive maintenance. These approaches enhance fault coverage by creating health indicators from many data sources, including existing process-control signals, expanding diagnostic capabilities beyond the common issues in rotating equipment. Collaboration between subject-matter experts and data engineers becomes imperative for the success of these advanced diagnostics.

Unlike visual diagnostic practices, which may be limited in scalability, data-driven AI approaches are repeatable and scalable. Once trained, machine learning algorithms deliver results, ushering in a new era of predictive maintenance practices.

Battery Dynamics in IIoT

While discussions on battery life often revolve around electric vehicles, the role and behavior of batteries in IIoT applications remain unexplored territory. The blog underscores the complexity of batteries, emphasizing their electrochemical nature and the dependence of charge delivery on environmental conditions.

Reviewing the specifications of wireless vibration-sensor products reveals that performance is contingent on various factors, including transmission distance, obstacles in the facility, radio protocol efficiency, and data volume. The blog sheds light on the impact of these factors on battery life, offering insights into the challenges faced in interpreting sometimes vague battery life specifications.

Vibration measurement, in particular, poses unique challenges. Analysts accustomed to analyzing waveforms might demand high-resolution measurements with longer sample lengths, resulting in more data transmitted than the current temperature. This, coupled with limited transmission configurations for vibration sensors, where 'sleep' mode dominates the electric current consumption, presents challenges that can affect battery life.

The blog substantiates these points with data from field testing wireless vibration sensors, illustrating the variability in battery life under different conditions. Factors such as distance and operating temperature are shown to influence the total charge consumed during 24 hours.

Mastering Condition Monitoring in the IIoT Era

Integrating IIoT capabilities into predictive maintenance marks a transformative shift in industrial landscapes. Remote and frequent measurements facilitated by IIoT enhance machine monitoring in challenging environments, providing a level of access that was once deemed difficult or unsafe. The data harvested through IIoT is not only data; it's a valuable asset that integrates into advanced data-processing platforms, promising an intricate and comprehensive diagnostic view, altering how we perceive and execute industrial maintenance.

In the quest for a reliable, end-to-end predictive maintenance solution, Le Price International's FailureProtect™ Service emerges as a frontrunner. As a CBM subscription service, it transcends traditional approaches, offering a holistic solution to predictive maintenance needs. Subscribers enjoy remote monitoring, diagnosis, and condition reporting backed by the expertise of our CBM professionals and reliability engineers. Our commitment to providing top-notch equipment, tools, and expertise without upfront costs positions it as a strategic choice for businesses navigating the complexities of predictive maintenance.

Source:

Spence, E. (n.d.). What You Need to Know About the Evolving Field of Condition Monitoring. Reliable Plant.