Vibration monitoring: the "stethoscope" for predictive maintenance of the Internet of Things

preface

According to relevant estimates, industrial manufacturers suffer staggering losses of $50 billion annually due to unplanned shutdowns, with maintenance costs accounting for 15% to 40% of total production costs. These alarming numbers highlight the importance of predictive maintenance in the Industry 4.0 era, which has become a hot topic in the industry. By implementing targeted prediction strategies, we are able to anticipate and prevent potential equipment failures in advance, significantly improving the operational stability of the equipment and effectively reducing maintenance costs.

In the practice of predictive maintenance, continuous condition monitoring plays an indispensable role, with vibration parameters being particularly critical. By monitoring the vibration of the equipment, we can gain insight into potential issues with multiple components, which not only affect the quality of the production process but may also lead to the shutdown of the production line.

Therefore, before delving into the core principles of vibration monitoring, let's briefly understand the enormous value that predictive maintenance brings to modern industry.

The Importance of Predictive Maintenance in the Context of Industry 4.0

Given that the cost of unplanned downtime is significantly higher than planned downtime, preventive maintenance strategies have been regarded as the gold standard in the industrial sector for over a decade. However, we must recognize that excessive or redundant maintenance activities carried out solely for conservative and cautious considerations are not an efficient and economical solution.

An in-depth study shows that up to half of preventive maintenance costs actually do not generate the expected benefits, which undoubtedly puts considerable pressure on the operating profits of enterprises. More importantly, not all causes of machine failures are directly related to the service life of the equipment. In fact, only 20% of failures can be attributed to normal wear and tear or aging of equipment, while the remaining 80% are caused by various sporadic events that are often difficult to predict and more likely to occur suddenly.

Therefore, we must realize that traditional maintenance plans based on fixed time intervals, although able to reduce the likelihood of failures to some extent, cannot guarantee that all problems can be detected and resolved in a timely manner. In order to more effectively respond to machine failures, we need a more intelligent and precise maintenance strategy, which is where the value of predictive maintenance lies. By monitoring the operational status of the equipment in real-time, especially key parameters such as vibration, we can predict and identify potential faults in advance, thereby developing targeted maintenance plans, avoiding unnecessary downtime losses, and improving the overall operational efficiency of the equipment.

Predictive maintenance, as a key part of the Industry 4.0 revolution, mainly achieves proactive diagnosis and prediction of faults through continuous monitoring and in-depth analysis of the health status of machines, effectively overcoming the limitations of traditional maintenance solutions. It relies on the new generation of IoT technology to collect real-time data on various events occurring inside the machine, forming a large and rich dataset.

By combining real-time data with historical asset data, and combining machine learning algorithms and predictive analysis techniques, different types of faults can be accurately identified, revealing their root causes and early symptoms. This data-driven analysis method not only improves the accuracy of maintenance activities, but also makes maintenance decisions more scientific and reasonable.

Once the predictive maintenance system detects a danger signal or abnormal pattern emitted by the equipment, it can immediately trigger the warning mechanism and arrange professional inspection and maintenance work. This timely response mechanism helps intervene before faults occur, avoiding catastrophic downtime events, ensuring the continuous and stable operation of the production line, reducing maintenance costs, and improving the overall operational efficiency of the enterprise.

Predictive maintenance>Preventive maintenance

The significant advantage of predictive maintenance lies in its reliance on a large amount of data on actual asset performance, rather than on speculative periodic maintenance plans.

According to Deloitte's research report, predictive maintenance performs well in improving production efficiency, reducing failures, and lowering maintenance costs. Specifically, predictive maintenance can increase production efficiency by an average of 25%, reduce failure rates by 70%, and also lower maintenance costs by 25%. This significant achievement not only helps enterprises improve operational efficiency, but also lays a solid foundation for their long-term development.

In addition, predictive maintenance can extend the service life of assets by accurately identifying potential issues with equipment and conducting targeted maintenance in a timely manner. This maintenance method can not only reduce the cost of equipment replacement for enterprises, but also create a longer value return cycle for the enterprise.

A pharmaceutical manufacturing plant has deployed vibration sensors on key equipment such as pumps, motors, fans, drain valves, filters, and HVAC pipelines, accurately identifying and solving 31 potential problems in just the first two months, highlighting the unique advantage of predictive maintenance in fault prevention.

The significant reduction in downtime for the pharmaceutical factory from 29% to 9% has directly improved the continuous operation capability of the production line, effectively ensuring a stable supply of drugs. At the same time, the interval between preventive maintenance has doubled, which not only reduces the frequency and cost of maintenance, but also improves the pertinence and efficiency of maintenance work.

It is worth mentioning that the pharmaceutical factory achieved outstanding results of zero downtime in the first half of 2019. Compared to the four downtime events that occurred the previous year, this achievement is undoubtedly the best proof of the effectiveness of its predictive maintenance strategy implementation. In addition, the new maintenance strategy has successfully extended the failure time of key components such as bearings and belts to once every six months. Compared to the previous 48 days, this improvement significantly extends the service life of the equipment and further reduces operating costs.

The core principle of vibration monitoring

Based on the signal characteristics generated by mechanical system vibration. When a mechanical system vibrates, strain or displacement is generated in the mechanical structure, which in turn triggers corresponding vibration signals. This signal can be collected through vibration sensors, such as acceleration sensors, displacement sensors, etc., which can convert mechanical vibration signals into electrical signals.

These electrical signals are processed through filtering, amplification, sampling, etc., and effectively analyzed and diagnosed through data analysis systems such as Fourier transform, waveform analysis, order analysis, spectrum analysis, etc., in order to obtain the vibration characteristics of the object. These features can be used to determine whether the device is operating normally, whether there are faults, and other related issues.

Vibration monitoring continuously monitors and analyzes the vibration signals of mechanical systems to detect signs of equipment failure in advance, providing strong support for preventive maintenance and repair, thereby avoiding accidents and improving the reliability and performance of mechanical equipment.

For rotating equipment widely used in various industries, vibration is not only one of the important signs of equipment failure, but also often a key indicator of impending failure. The abnormal increase in vibration intensity can generate unnecessary harmful forces in equipment components, posing a serious threat to their service life and quality. If these vibration signals are ignored as warnings, equipment failures and production line interruptions are almost unavoidable, which will lead to production losses, cost increases, and damage to the company's reputation.

Therefore, equipping advanced sensor systems such as accelerometers on rotating equipment has become a crucial task. These sensors are able to capture the vibration patterns of the device in real time and convert them into analyzable data. By continuously monitoring this data, manufacturers can promptly detect any abnormal changes in vibration patterns and accurately determine the operating status of the equipment.

With the deepening of the Industry 4.0 wave, predictive maintenance strategies for the Internet of Things based on vibration monitoring will make significant and substantial progress in helping manufacturers optimize asset uptime and improve efficiency.

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