Industry 4.0 is here; this also substantiates the start to a new era of industrialization where machines are moving toward being autonomous. For asset-intensive enterprises, it is imperative to keep watch on various processes such as material production, overall equipment efficiency and asset management. The rise in adoption in using sensors, edge devices and artificial intelligence is a product of growing costs for managing assets. Experts have predicted that businesses would invest near half a trillion dollars for asset management by the end of the next decade.
By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.
Here is where machine learning comes handy. Though its path is marred with numerous challenges, it still commands a success rate better than any of its contemporary solutions. It might sound weird, but the fact remains that researchers are spending billions on the research and development of a working machine learning technology capable of aiding asset management for industries. Some of the top scientists have spent more than three years simply developing a machine learning algorithm, and to date, the success rate of machine learning technologies clocks below 10%.
Dead investment or exemplary foresight?
In all the commotion, the main question that arises is, “Why is the industry still pursuing such a venture?” The answer to that question lies in machine learning capabilities. Companies are facing challenges to ensure that their machines are running at a heightened efficiency level. For this, they will require the ability to monitor their assets remotely. IIoT offerings have made it possible to reach a certain success rate, but machine learning holds the key.
Data is the future
Big data has taken off at a never-before-expected rate, and this might be the catalyst for the success of machine learning. Assets in an industry are tough to manage, but automation and the use of data through IIoT has provided a new pathway towards better asset management. And not to mention, the additional challenge of the human error element through increased human intervention. But machine learning promises to do away with all of this, as it has been hyped to use machine-generated data for the benefit of those very machines ensuring optimum asset management at all times.
Having a feedback cycle with human intervention is not only time-consuming, but it also increases the chance of errors through miscommunication. Instead, if a machine itself can analyze data and provide alerts at the right moment, the issue of asset management is sorted forever. The machine learns, analyzes and adapts itself to ensure maximum output is realized every single time. Using algorithms, machine learning allows enterprises to unlock hidden insights from their asset data. For instance, a forecast regarding an asset failure can help in scheduling preventive maintenance of yet to fail asset. Such machine learning algorithm-driven predictive analytics software can enable enterprises to make fully vetted and well-timed decisions towards improved asset management.
Though many machine learning algorithms have been around, an ability to apply complex calculations to big data automatically, faster and faster, over and over is the latest development. These machine learning technologies can enhance growth for an enterprise to yield substantial profits through optimum utilization of resources at hand, all made possible by machine learning. A few of the machine learning algorithms that are being applied widely include linear regression, decision tree, logistic regression, random forest, naive Bayes classifier algorithm, neural networks and gradient boosting.
The following are a few of the advantages of machine learning in asset management:
- Highest uptime/runtime and improved machine performance
- Constant machine health monitoring
- Advanced analytics of all assets drilled down to various levels such as machine, plant, facility, and so on
- Reduced consumption of raw materials and resources such as air, water, heat and electricity
- Facilities and operator performance monitoring
- Live alerts, reports and detailed data logs for each instance
In conclusion, industry experts have deemed machine learning as an unrealistic absurdity and all the other negative adjectives you could think of, but the promise of improving our future and simplifying lives through improved asset management is what keeps the spark going.
Read how a utility service provider and meter manufacturer leverages Azure Machine Learning to remotely monitor its IoT-based smart water meters. Notably, the company reduced water consumption by more than 30% by effectively managing meter failures and water leakages.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.