Software : Data & Analytics : Machine Health
Viking Analytics was born to bring together people, data and insights, in a way that doesn’t require process and operations professionals to become data scientists. We bridge the gap between domain experts and data scientists by scaling data contextualization with our platform MultiViz, that enables building of reliable and scalable predictive maintenance applications without advanced knowledge in AI or an army of AI engineers. Founded in 2017 in Gothenburg, Sweden, by a team of experts in signal processing, machine learning and software systems, Viking Analytics has been recognized as one of the startups driving AI innovation in Europe by AI Sweden.
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.
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.