Predictive Maintenance

Asset performance management is considered an important operational metric across the housing sector – optimising reliability, availability and safety. Organisations within the housing sector are transitioning from reactive to preventive to predictive maintenance strategies, resulting in significantly reduced asset downtime and notable increases in productivity, efficiency, and profitability.

Maintenance repairs can be expensive and time consuming, therefore it’s important to put processes in place in order to minimise and prevent them. Predictive maintenance software uses condition monitoring tools to monitor the performance and condition of equipment and determine when maintenance should be performed.  The software predicts possible defects and estimates when they require fixing before a likely failure. As part of an organisation’s maintenance strategy, predictive maintenance differs from preventative maintenance whereby routine repairs and maintenance are scheduled to take place.

Combined with the Internet of Things (IoT), predictive maintenance software evaluates the performance of an asset providing information in real time. This information is provided through capturing data from IoT enabled sensors. This data is then analysed and actioned to prevent failures occurring. Predictive maintenance sensors include monitoring equipment which can detect a variety of things such as vibrations, temperature or humidity.

Field driven studies also demonstrate that unplanned maintenance downtime bears a heavy financial burden – costing an estimated £40 billion yearly. Data driven, analytical solutions such as IoT and mobile sensors are enabling a new approach to asset management strategy across the sector, allowing organisations to circumnavigate the operational and financial burdens associated with traditional break-fix models.

Asset management strategies often fall into one of the following four categories, each with varying challenges and benefits; Reactive maintenance, Planned maintenance, Proactive maintenance, Predictive maintenance.