Reactive, avoidable equipment repairs are a leading contributor to lost productivity in industrial manufacturing operations. Parts with an average selling price of just a few dollars can cost manufacturers many times that in repairs and unrealized revenue once they fail. In a worst-case scenario, undetected faults can cascade through the system, causing widespread damage and triggering major and expensive production outages.
Historically, manufacturers have relied on preventative measures to keep their production floors up and running. While an improvement over after-the-fact repairs, preventative maintenance typically requires costly service contracts and ultimately is limited in its ability to ensure continuous equipment up-time. Consider the rotor bearings that turn the blades of a 200-foot wind turbine. Emergency repairs and spot maintenance are expensive—and in this case may even be dangerous as technicians are required to work at high elevation. Moreover, if the turbine is tied into the power grid of a local municipality, unscheduled downtime could result in lost energy production and possibly disrupt electrical service.
A new industrial sensing technology is helping manufacturers optimize their equipment by using a form of predictive maintenance that anticipates part failures. While many forms of industrial sensing exist, vibration sensing is perhaps the most effective and efficient. According to a study by Lindsay Engineering, a provider of predictive maintenance products and services based in Camarillo, Calif., vibration sensing delivers three times the return on investment of steps such as regularly changing gear or motor oil.
Benefits of Vibration Analysis
Vibration analysis is commonly used in rotating machinery to detect loose or worn bearings, equipment misalignment, or low fluid levels that might cause a change in vibration. Typically this vibration will occur at frequencies between 6KHz and 10KHz.
Other data is also available at higher frequencies, but is typically very difficult to measure due to magnitude of the response and requires expensive technologies such as ultrasound. By measuring that frequency range and monitoring changes in the response, manufacturers can schedule maintenance or bring down equipment when it is most convenient and before the part is damaged to the point where it can cause even more costly secondary system failures.
Additionally, various statistical formulas, such as mean time to failure (MTTF) and mean-time- between-failures (MTBF), can be used to forecast the life of the system. Utilizing those formulas, along with raw data from the system will allow customers to focus directly on the potential problem. For example, using MTTF you find out that a certain bearing has a high degree of failure, you can use a vibration sensor to carefully monitor that particular machine and bearing to make sure that the failure does not happen.
The two most common means of implementing industrial vibration sensing are to retrofit existing equipment with sensor systems or contract with a third-party service that conducts regularly scheduled equipment tests. The latter option can be expensive, and periodic checks are less effective than mounting sensors directly to the equipment. The system-mount approach affords manufacturers continuous monitoring but this option has also had historical limitations.
The majority of today’s vibration sensors typically operate below 5 kHz of bandwidth, which is significantly lower than the frequency at which most equipment failures can be detected. Additionally, conventional sensors are most often based on high-voltage piezoelectric technology that requires a bulky metal-can package, and the devices demand frequent calibration and are not easily manufactured in large volumes.