Detecting and monitoring operational modes in a machine is a very effective way of getting a precise view of the asset’s health conditions. In a machine, different operational modes can reflect different usages, a production process, an on-off state, or a developing failure.
But besides reflecting the overall state of…
One of the fundamentals of condition monitoring is being able to trust the data you get from your sensors. …
Bayesian methods have become widely used in machine learning and pattern recognition. Talking simplistically, traditional — or ‘frequentist’, statistics see probability as the limit of relative frequencies of events as the number of trials increases, assuming a fixed set of distribution parameters. Bayesian statistics, on the other hand, is more…
As part of predictive maintenance techniques, condition monitoring is used to detect anomalies and predict machinery’s health in real-time. Sensor data is used to verify whether a component failure is likely. Some failure occurs gradually and can be prevented by routine inspections and examinations. In contrast, other types of failures…
By Sergio Martin-del-Campo, Data Scientist at Viking Analytics.
Even companies that are playing in the advanced league of the condition monitoring game may face the challenge of knowing which machines need to be prioritized for maintenance. …
By Fredrik Wartenberg, Data Scientist at Viking Analytics.
Vibration monitoring is key to performing condition monitoring-based maintenance in rotating equipment such as engines, compressors, turbines, pumps, generators, blowers, and gearboxes. …
Predictive maintenance, despite its measurable results and successes, has been suffering from lack of scalability. Models designed and trained for a given machine with a particular set of sensors often had to be retrained before being used with other similar machines. In addition, the quantity and variety of machines in…
In the manufacturing sector, the importance of digitalization has been evident, even before all the disruption brought by Covid-19 and the need to quickly adapt to new challenges. According to a research done by Deloitte , in the United States, “86 percent of manufacturers believe that smart factories will be…
The science behind Industry 4.0: theoretical foundations of Deep Learning via Sparse Representations
By Sergio Martin-del-Campo, Data Scientist at Viking Analytics and leading expert in the areas of data science and machine learning for industrial processes.
Each day, it increases the frequency we hear terms such as artificial intelligence (AI), machine learning (ML) and deep learning (DP). A lot of time these terms…
