Simple solutions to achieve Operational Excellence and Output Efficiency


Condition Monitoring - CBM & PdM

Condition Monitoring (CM) technologies have been extensively proven in the Aerospace, Defence and Energy sectors over the last 25+ years, through the utilisation of Condition Based Maintenance (CBM) and Predictive Maintenance (PdM) applications to optimise and improve operational efficiency. The extensive deployment of CBM and PdM has generated significant operational and maintenance savings, as well as contributing to increased operational efficiency. Reductions in maintenance costs of between 10% and 50%, and increases in asset availability of 50%, are not uncommon. However, the cost of these technologies/applications is often a barrier to entry for the wider roll out of CM to other sectors, such as Manufacturing; Industrial Processes; Agriculture; Buildings, Land Vehicles; etc… where complex machinery is critical to efficiency and productivity. Installation cost, machine learning requirements and specialist skill requirements have meant the adoption of CBM and PdM technologies has been slow in sectors where an overarching safety case is not a key driver. Even so, there is tremendous interest in applying the benefits of CBM/PdM in these sectors and many system solutions now exist to apply basic CM technologies to even the simplest of machines - but there is still a gap between what is affordable and what can generate validated results to achieve real benefit and real return on investment (ROI). Typically, the general entry level CM solutions provide limited diagnostic capabilities, and a means of exploiting the full potential of the technology at an affordable price point is still required - this is where Prism Nova can provide innovative new solutions allowing traditional methods and technologies to be scaled to a much wider audience. 

Unplanned downtime of assets and high cost of maintenance is a real challenge in industry. CBM is crucial, but lacks foresight – typical diagnostics focus on current condition and early detection of degradation based on fixed thresholds without providing a future horizon. Its output is therefore knowing ‘that something’ will happen at some point in the future, but not necessarily ‘when’.

The term CBM, generally refers to proactive maintenance which is carried out when real-time data monitoring and diagnostics produces indicators consistent with the fact that either:

                 - the equipment is likely to fail
                 - the equipment performance/capability is deteriorating

By using CBM, potential failures can be averted by planning maintenance once an issue is detected. Although the CBM itself incurs additional operating costs, overall maintenance costs can be reduced by averting unplanned failures. With experience the maintenance can be planned for optimum convenience and minimal risk.

By adding prognostic capabilities, an intelligent system can enhance the value of CBM by predicting ‘when’ failure or degradation is likely to occur, hence providing an estimation of Remaining Useful Life (RUL), and further assisting maintenance planning. Prognostics is the science of forecasting when assets will stop being able to perform their intended functions. It means that you can properly perform PdM, with the right information and it is undoubtedly the future of condition monitoring. Unlike CBM alone, with prognostics the output is knowing approximately when ‘that something’ will happen.

Typical CBM data, when combined with prognostics, can be used to determine:

  • When the performance of an asset starts to deviate from ‘normal’ condition.
  • The Remaining Useful Life (RUL) on an asset.
  • The nature of an impending failure.
  • The urgency for maintenance activity, enabling maintenance to be scheduled at the optimum time to minimise costs.