Free-flowing data is fast becoming the chief currency in this smart environment. Even so, data is pointless unless it empowers people to make better business decisions.

Senseye today announced the launch of Attention Index®, a pioneering technology developed by Senseye and now available within Senseye PdM.

Reducing downtime may be at the top of your wish-list when opting for a Predictive Maintenance regime, but there are other big rewards on offer. Don’t overlook the potential benefit of eliminating over-maintenance.

Manufacturing has long been an industry that places peak performance, operational efficiency and quality targets at the top of their agenda. Leveraging data and the Industrial Internet of Things (IIoT) can support these objectives.

Condition monitoring has evolved over the past 30 years.

What is the next step in the world of predictive maintenance? The answer: prognostics. Prognostics is the science of forecasting when your assets will stop being able to perform their intended functions. With prognostics in place you can properly perform PdM which is undoubtedly the future of condition monitoring.

The idea of predictive maintenance can be daunting for some manufacturers as it represents a significant cultural shift in how they plan, prioritize and perform maintenance activities. The benefits of doing so can be huge but if this shift isn’t properly managed, there can be serious consequences.

In a competitive marketplace enhancing machine reliability by implementing a condition monitoring program can make all the difference. But realizing your ambitions takes planning, commitment & continuous improvement. We look at the common pitfalls & provide ways you can realize its potential.

Making the decision to transition to automated condition monitoring shouldn’t be taken lightly. Far from a standalone maintenance project, automatic condition monitoring requires a shift in the entire organizational culture to support dramatic process, attitude and skillset changes.

Reducing unplanned downtime in factories is crucial to reducing overall maintenance spend and the total cost of asset ownership. Achieving this at scale however is a significant challenge and can only be achieved through the intelligent use of machine learning driven predictive maintenance solutions

There are subtle but crucial differences between detection, diagnostics and prognostics when discussing machine health. Whilst you don't need to know the details, it's important to understand the differences to apply to your own industrial condition monitoring project for maximum benefit and ROI.

Prognostics is particularly exciting as it means understanding the Remaining Useful Life of your machinery however it's easy to get wrong and be unsuccessful.

Analysing condition monitoring data manually is beneficial but this method limits scalability whilst coming with great expense. Automated is best but how / when

Maintenance practises have changed significant in a fairly short time. No planning has given way to scheduled planning, giving way to predictive maintenance.

Investing the correct amount in condition monitoring can be a challenge as it's easy to spend too much and be disappointed with the results.

Predictive maintenance can result in avoiding between 30-50% of downtime that occurs during preventative maintenance but not many companies have adopted it.

Different techniques come under the label 'condition monitoring'. In this overview we explore where it came from, what is done and how effective it is.