Vibration Analysis
Assembly Line
Vibration Compensation Improves Robot Performance
Any machine that moves deals with vibration, whether it’s a 3D printer, machine tool or robot. It’s typically managed to the point that many do not even realize that vibration is a problem, but with proper strategies to deal with vibration, machines can run faster and more efficiently. Ulendo is an Ann Arbor, Michigan-based startup that produces software solutions for manufacturing automation. The company launched with a product that’s now called Ulendo VC (for “vibration compensation”), which applies an algorithm to fused filament fabrication (FFF) 3D printers, counteracting the machine’s vibration patterns and enabling it to run up to five times faster while maintaining part quality.
Since the launch of Ulendo VC, the company has expanded into laser powder bed fusion (LPBF) additive manufacturing with Ulendo HC (heat compensation), which optimizes the path of an LPBF machine’s laser to reduce heat-induced deformation and stress. It’s also offering Ulendo Calibration-as-a-Service, which applies its vibration compensation to end-users’ extrusion 3D printers (as opposed to working with machine suppliers, as is the case with Ulendo VC). The company is also broadening its reach to include machines outside the 3D printing space with its fourth product: vibration compensation for robots.
Ulendo’s solution actually compensates for a machine’s vibrations instead of working around them. It does this by “tricking the machine,” as Okwudire describes it. Traditionally, Ulendo measures the machine’s vibration patterns and creates a “calibration map” that predicts how the machine will vibrate and calculates how to offset that motion. So, if a machine veers to the left instead of going straight, Ulendo VC-R (vibration compensation for robots) adjusts the path to the right, which cancels out the leftward motion and causes the machine to go straight.
Vibration in Rotating Machinery: Analysis & Solutions
Unmanaged vibration introduces risks like mechanical wear, increased maintenance costs, and safety hazards for operators. Over time, this can result in machine downtime, loss of productivity, and higher operational costs. Addressing vibration early in the design phase or through continuous monitoring and vibration simulation helps maintain the reliability and performance of rotating machinery.
Hazleton Pumps, a manufacturer of heavy-duty pumps and pump systems, faced vibrational problems with one of their large, installed pumps weighing approximately 9 tons and operating at 800 RPM. Despite attempts to manually stabilize the pump with clamps, the vibration persisted, prompting the company to hire independent engineers. The engineers recommended significant modifications, including adding 500 kg of steel reinforcements, adjusting the subframe, and redesigning the bearing-to-shaft assembly, with an estimated cost of $40,000 per pump.
Instead, Hazleton turned to SimScale’s structural analysis tools to conduct a detailed multi-body modal analysis on the entire pump assembly. The simulation revealed that the eigenfrequency of the structure was around 780 RPM, meaning the pump was operating dangerously close to this resonance frequency. Equipped with this insight, Hazleton modified their operational procedures to avoid running the pump below 950 RPM, thus avoiding resonance-induced vibrations. They also implemented more cost-effective solutions, such as adding square tubing to the subframe, dramatically reducing costs compared to the original recommendations.
Vibration Suppression Methods for Industrial Robot Time-Lag Filtering
This paper analyzes traditional vibration suppression methods in order to solve the vibration problem caused by the stiffness of flexible industrial robots. The principle of closed-loop control dynamic feedforward vibration suppression is described as the main method for solving robot vibration suppression. This paper proposes a method for time-lag filtering based on T-trajectory interpolation, which combines the T-planning curve and the time-lag filtering method. The method’s basic principle is to dynamically adjust the trajectory output through the algorithm, which effectively suppresses the amplitude of the harmonic components of a specific frequency band to improve the vibration response of industrial robot systems. This experiment compared traditional vibration suppression methods with the time-lag filtering method based on T-trajectory interpolation. A straight-line method was proposed to measure the degree of vibration. The results demonstrate that the time-lag filtering method based on T-trajectory interpolation is highly effective in reducing the vibration of industrial robots. This makes it an excellent option for scenarios that demand real-time response and high-precision control, ultimately enhancing the efficiency and stability of robots in performing their tasks.
Eliminating Downtime: How the TRACTIAN Vibration Sensor Makes a Difference
Identifying Bearing Faults Through Vibration Analysis
Smart Trac: AI-Assisted for Predictive/Condition-Based Vibration Monitoring
Vibration analysis for equipment degradation assessment and preventive maintenance
Vibration analysis is an effective way to identify defects and is also considered an early and reliable indicator of defects. Such information can help improve asset performance and reduce maintenance downtime. At Hitachi India R&D, we decided to look at how vibration analysis could be applied in equipment degradation assessment and support preventive maintenance in wind turbines. Firstly, wind turbines are a popular renewable energy source and maintenance & operations of these high value assets are expensive. There are further challenges in the operations of wind turbines as they operate in a remote location and equipment are hoisted at tower as high as 100 meters. Remote monitoring & early fault detection allows wind farm operators to take timely corrective steps and reduce energy and revenue loss. SCADA based monitoring is a standard process for most operators. Secondly, the science behind the vibration analysis of gearbox applies to any rotating equipment and our work can expand to products like pumps, motors etc in industries like automotive, water, energy etc.
Koch Ag & Energy High Value Digitalization Deployments Leverages AWS
This application uses existing plant sensors, Monitron sensors, Amazon Lookout and SeeQ software to implement predictive maintenance on more complex equipment. The goal accomplished was successfully implementing predictive maintenance requires data from thousands of sensors to gain a clear understanding of unique operating conditions and applying machine learning models to achieve highly accurate predictions. In the past modeling equipment behavior and diagnosis issues requiring significant investment in time money inhabiting scaling this capability across all assets.
Detecting different fault locations on a bearing
We are going to use the popular Bearing Vibration Data Set from Case Western Reserve University as a benchmark to demonstrate how different bearing conditions and faults can be properly correlated to a different operational mode, and ultimately to the automatic identification of healthy and faulty operational conditions.
MultiViz Vibration’s Mode Identification feature is powered by our Automatic Mode Identification (AMI) unsupervised algorithm for multivariate time series analysis. It performs multidimensional data segmentation and clustering in time series data, such as waveform vibration signals. It detects time periods in which the data exhibits a similar behavior and reports these periods as belonging to the same operational mode.
Operational modes are often correlated with typical conditions of an asset, like on/off, load conditions or fault states. Thus, the identification of different modes when the behavior of the machine has remained the same, can point to the appearance of a fault in the machine.
Predictive Monitoring: Gas Turbines Demo
Machine vibration analysis benefits for manufacturers
Vibration analysis allows early detection of wear, fatigue and failure in rotating machinery because vibration occurs in all rotational assets, but generally highlights an issue discovered by higher readings and particular frequencies, mostly as the result of wear and tear but also as a consequence of poor maintenance practices. Vibration builds and leads to equipment failure.
Vibration analysis identifies potential problems and a predicted time to failure (in some cases up to one year in advance of equipment failure) to enable replacement parts to be ordered in a timely way and helping to reduce unexpected downtimes.
Machine Learning Keeps Rolling Bearings on the Move
Rolling bearings are essential components in automated machinery with rotating elements. They come in many shapes and sizes, but are essentially designed to carry a load while minimizing friction. In general, the design consists of two rings separated by rolling elements (balls or rollers). The rings can rotate can rotate relative to each other with very little friction.
The ability to accurately predict the remaining useful life of the bearings under defect progression could reduce unnecessary maintenance procedures and prematurely discarded parts without risking breakdown, reported scientists from the Institute of Scientific and Industrial Research and NTN Next Generation Research Alliance Laboratories at Osaka University.
The scientists have developed a machine learning method that combines convolutional neural networks and Bayesian hierarchical modeling to predict the remaining useful life of rolling bearings. Their approach is based on the measured vibration spectrum.
Using Machine Learning to identify operational modes in rotating equipment
Vibration monitoring is key to performing condition monitoring-based maintenance in rotating equipment such as engines, compressors, turbines, pumps, generators, blowers, and gearboxes. However, periodic route-based vibration monitoring programs are not enough to prevent breakdowns, as they normally offer a narrower view of the machines’ conditions.
Adding Machine Learning algorithms to this process makes it scalable, as it allows the analysis of historic data from equipment. One of the benefits is being able to identify operational modes and help maintenance teams to understand if the machine is operating in normal or abnormal conditions.