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.