What you’ll learn:
- How to use vibration and oil analysis to monitor manufacturing equipment.
- How to gather data needed for valuable insights and use predictive maintenance to prolong the equipment's life.
- Unconventional sources from which to collect energy.
At one time, equipment operators did not have a practical way of knowing about a looming breakdown before it was too late. Throughout the life of a machine, the best possible option would have been to perform maintenance at routine frequencies, hoping to mitigate a failure event in time.
We've come a long way since those days. The applicable technology for condition monitoring has evolved in ways that allow facilities to streamline maintenance while increasing equipment reliability. Presented here are some innovations over the past decades that have revolutionized how automotive manufacturers can keep an eye on equipment.
Generating Insights from Vibration Analysis
Dynamic components subject themselves to additional strains and stresses compared to static structures. Analyzing vibration patterns provides insights into misalignments, unbalanced weight distribution, and many other, potentially compromised configurations.
A typical vibration-analysis setup works by installing an accelerometer sensor to capture fluctuations in the position of a particular point. Modal analysis is one of the most used applications of vibration analysis. It describes the characteristics of vibration by providing insights into natural frequencies and normal modes of a design. Without such investigation, a structure risks running applied loads dangerously close to the natural frequency of a system, resulting in resonance and eventual failure.
Automotive manufacturing concerns several moving components that are continually at the receiving end of forces and deformations. Vibration analysis becomes an integral part of the design of the elements that comprise the final products.
Damper and suspension designs are some of the prominent applications of vibration analysis. Engine mounting and chassis structures also require rigorous vibration testing. Moreover, the equipment to produce parts can benefit from condition-based vibration analysis to sustain reliable operations.
The moving parts in manufacturing equipment, such as bearings and gears, benefit from vibration analysis, too. Complex components like robotic systems and rotating equipment, powered by heavy-duty electric motors, also can generate insights from vibrations.
Vibration analysis is one of the most versatile condition-monitoring techniques for product design and overall equipment maintenance. Conventional analysis is already indispensable in and of itself, but it’s even more effective with the application of industrial IoT (IIoT): Installing devices that measure vibrations across a plant can expand condition-based monitoring from production to maintenance.
Reducing Friction with Advanced Oil Analysis
All facilities with moving parts will rely on some form of lubrication. Until we discover better ways to reduce friction, oils and lubricants will remain the essential unsung heroes of smooth production. Aside from their primary task, they’re also useful diagnostic tools for the condition of the equipment that uses them. Analyzing oil samples has been around for decades, but the techniques and levels of analysis have significantly improved over recent years.
What used to be a bulk analysis of oil characteristics now employs more intricate methods. Advanced breakdown spectroscopy, for instance, allows for the identification of particles at a microscopic level. Modern techniques can determine specific alloy classification of the debris in oils and lubricants, which improves insights into the severity of an existing or impending failure.
The industrial equipment that makes the most of oil analysis includes the assets that require regular oil and lubricant changes. Gearboxes for power transmission will typically employ stringent lubrication requirements. High-capacity transformers and electrical distribution equipment typically utilize insulating oils that enable the same level of analysis. Other common forms of equipment known to use oil systems include engines, bearings, and rotating components.
Obtaining Information Through Autonomous Data Collection
Condition-based strategies rely heavily on data. Innovations in the way we collect data have a direct impact on the effectiveness of an overall approach. While still in the early stages, clever implementations that are able to collect data autonomously can revolutionize how we monitor equipment.
Microfliers are nature-inspired devices that offer promising, unprecedented ways to gather data. Their design utilizes a photodiode to generate a photocurrent from light. It then couples with a supercapacitor to store the accumulated charges, which allows for battery-free data transmission.
Essentially, such widgets mimic a more traditional autonomous data-collection system. More established systems for automated data collection employ a measuring implement, such as a thermocouple for temperature gradients or an oscillation sensor for vibration patterns, to collect raw data.
These discrete data points are converted into digital information, transmitted through the cloud, and placed into a centralized repository. After transforming the data into a form that facilitates analysis, advanced software and algorithms can produce insightful descriptions of equipment conditions. In the context of a condition-based monitoring system, ingenious designs pave the way for the future of data gathering. As the capability to analyze copious amounts of data further expands, so do the ways and means to obtain information.
Transforming Data into Information with Predictive Maintenance
Gathering extensive data, and having easily accessible relevant information about a facility, are only as valuable as the actionable insights they provide. A predictive-maintenance philosophy takes condition monitoring a step further by transforming information into specific recommendations on how to prolong the life of assets and the overall productivity of a plant.
As with any industrial plant, a typical automotive manufacturing facility will require servicing several pieces of equipment that are critical to production. Pneumatic systems for lifting and moving parts around, complex cyber-physical robots in assembly lines, and chemical treatment baths are only some of the many integral pieces of a plant. For each process component, maintenance tasks and servicing requirements may vary widely.
Digital transformation in manufacturing enables facilities to consolidate their maintenance needs into a platform that can collect equipment performance data, analyze historical patterns, and predict ways to optimize operational conditions. The flow of information starts from sensors that monitor the state of a machine through real-time data collection.
Maintenance-management software can then ingest these discrete data points with the capability to employ artificial-intelligence and machine-learning technologies. The resulting outputs are data-driven insights that combine the analysis of historical data with algorithms that suggest potential failure outcomes, along with ways to mitigate them.
Collecting Energy from Unconventional Sources
If you’re a designer of embedded systems, this concept might be of interest.
The backbone of a condition-based strategy is a reliable network of sensors. For power-sensing devices and transmitters, especially for remote applications, batteries are among the top choices for energy. While these are highly effective, emerging technologies are exploring options for a more continuous and uninterrupted power source for sensors to keep performing what they do best.
The key to viable energy harvesting is choosing the right environment for a suitable application. Compared to traditional energy storage and supply devices, obtaining energy from surroundings yields significantly lower values. However, powering wireless sensor nodes provides a perfect application opportunity given their low-duty-cycle requirements.
A concept relating to energy harvesting involves utilizing ambient vibrations into usable electricity. By coupling some device with an oscillating member, its displacement with respect to a fixed coil generates electricity that can act as a power source. A mechanical resonator picks up the kinetic vibrations in its surroundings to induce relative movement and, consequently, a change in electromotive voltage.
In theory, a resulting energy-harvesting system can supply the electrical requirements for remote sensing devices. In turn, these sensing devices can include measuring implementations that collect information about the condition of a piece of equipment.
Conclusion
By leveraging condition-monitoring technologies, automotive manufacturing facilities can assess the state of their equipment. Without that, they run the risk of either experiencing catastrophic failure or performing wasteful maintenance in an attempt to prevent breakdowns. On the other hand, investing in monitoring tools provides a baseline of current performance and, more importantly, opens opportunities for predictive strategies.