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. For example, how can a professional responsible for the maintenance of compressors in a company realize which ones need to go to the top of priorities list?
The simple answer might be “by looking at the data collected by the sensors”, but as we have already covered in our blog post about Automatic Mode Identification (AMI) of machines, it is a difficult and time-consuming…
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. 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.
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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 a factory makes developing, training, and maintaining models for each machine a challenge. Therefore, it‘s natural that predictive maintenance became limited to the most valuable assets in a plant.
But that is quickly changing.
Traditionally, vibration analysis requires a vibration expert, detailed knowledge of the machines, and significant contribution from…
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 main driver of competition by 2025. Furthermore, 83 percent believe that smart factories will transform the way products are made.”
Factory leaders have a broad range of opportunities to choose from, and sometimes it can be hard to decide which technology or trend to prioritize. So here are five…
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 are used interchangeably and the relationship between them is not always clear. Artificial intelligence is the intelligence demonstrated by machines to creatively solve problems. Machine learning is a subset of artificial intelligence that enables computer programs to learn from experience without being explicitly programmed. …
By Fredrik Olsson, Senior Python developer and Software Architect at Viking Analytics
In a recent text, my colleague Arash Toyser wrote about data and its relation to value using a very intuitive and informative analogy of gold and goldmines, making the point that raw data is the goldmine and the extracted insights are the actual gold. Continuing the analogy, one can argue that gold in itself has no inherent value. …
It is not an exaggeration that knowledge is one of the most valuable assets a company has. Even with the growing trend of automation, technical knowledge can not be just directly bought off a shelf, as it heavily relies on the experience of more seasoned professionals. Not managing it properly can lead to loss of knowledge and several hours of relearning to build up the knowledge base that already exists.
To make a simple comparison; imagine a technical breach resulted in all the documents in the internal network of your company being deleted. Everything, from contracts to customer relationship history…
By Arash Toyser, co-founder and data scientist at Viking Analytics
Many industrial companies have been collecting data for several years if not several decades with varying degree of success in terms of the quality of the data collected. We asked ourselves, if data is the new gold, why are companies struggling with converting it to real tangible revenue? Afterall, this was the reason we started Viking Analytics; to help industrial companies get to actionable insights from their data. We discovered this is a difficult and a complex question to answer. There is a wide range of reasons varying from technical…
