What is planned preventative maintenance (PPM)?<\/strong><\/h3>\nRather than waiting until assets break down and fix them reactively, a much better method is to schedule PPMs at regular intervals throughout the year. This could entail a visual inspection, a clean, lubrication, a filter change or part replacement, for example. Setting up PPMs<\/a> using CMMS software is easy, and allows them to be combined with other works due at the same time and in the same area to make more effective use of team hours.<\/p>\nHowever, there is a risk of over-maintaining, which can shorten the life of the asset (such as over-lubricating a bearing in a pump), use up spares unnecessarily and waste the time of the engineer where the work was not required. These are generally risks worth taking, but FMs could also work towards a predictive maintenance strategy.<\/p>\n
What is condition-based or predictive maintenance (PdM)?<\/strong><\/h3>\nPredictive maintenance<\/a> uses sensors and indicators to predict when an asset is due for maintenance. For example, measuring the run time of an escalator and generating a service for xxx number of hours according to manufacturer guidelines; using a BMS to be alerted of readings outside of pre-established limits; or installing sensors on assets to monitor temperature, pressure, humidity, vibration frequency, and more. As these readings start to show deterioration or produce an abnormal result, then maintenance can be scheduled before any breakage occurs.<\/p>\nWhat is data-driven maintenance (DDM)?<\/strong><\/h3>\nData-driven maintenance is a type of predictive maintenance but goes one step further. DDM uses information from multiple sources to intelligently determine when an asset needs maintaining. This could include maintenance history, previous breakages, downtime, and even other installations of the same asset elsewhere to identify operating patterns and predict when a service should happen.<\/p>\n
In order to analyse this data, generative AI is required \u2013 something the industry as a whole has been reticent to jump on board with. And perhaps justifiably so? Budgets remain static but the expectation for FM productivity and transformation is through the roof \u2013 who can afford to implement every new gadget? But as Paul discusses with FMJ, AI can analyse data with such granularity to extract patterns and create actionable strategies that it really is a game-changer for FMs in terms of resource efficiency. And contrary to common perceptions, AI and IoT solutions are not costly and complex to implement, meaning FMs can start acting on insights quickly to drive meaningful operational gains.<\/p>\n
For more information on types of planned and predictive maintenance, as well as our AI platform Senslinc, contact us here<\/a>.<\/p>\nKeep up to date on the latest industry and technology developments: sign up to receive SWG\u2019s blog delivered straight to your inbox:<\/p>\n
However, there is a risk of over-maintaining, which can shorten the life of the asset (such as over-lubricating a bearing in a pump), use up spares unnecessarily and waste the time of the engineer where the work was not required. These are generally risks worth taking, but FMs could also work towards a predictive maintenance strategy.<\/p>\n
What is condition-based or predictive maintenance (PdM)?<\/strong><\/h3>\nPredictive maintenance<\/a> uses sensors and indicators to predict when an asset is due for maintenance. For example, measuring the run time of an escalator and generating a service for xxx number of hours according to manufacturer guidelines; using a BMS to be alerted of readings outside of pre-established limits; or installing sensors on assets to monitor temperature, pressure, humidity, vibration frequency, and more. As these readings start to show deterioration or produce an abnormal result, then maintenance can be scheduled before any breakage occurs.<\/p>\nWhat is data-driven maintenance (DDM)?<\/strong><\/h3>\nData-driven maintenance is a type of predictive maintenance but goes one step further. DDM uses information from multiple sources to intelligently determine when an asset needs maintaining. This could include maintenance history, previous breakages, downtime, and even other installations of the same asset elsewhere to identify operating patterns and predict when a service should happen.<\/p>\n
In order to analyse this data, generative AI is required \u2013 something the industry as a whole has been reticent to jump on board with. And perhaps justifiably so? Budgets remain static but the expectation for FM productivity and transformation is through the roof \u2013 who can afford to implement every new gadget? But as Paul discusses with FMJ, AI can analyse data with such granularity to extract patterns and create actionable strategies that it really is a game-changer for FMs in terms of resource efficiency. And contrary to common perceptions, AI and IoT solutions are not costly and complex to implement, meaning FMs can start acting on insights quickly to drive meaningful operational gains.<\/p>\n
For more information on types of planned and predictive maintenance, as well as our AI platform Senslinc, contact us here<\/a>.<\/p>\nKeep up to date on the latest industry and technology developments: sign up to receive SWG\u2019s blog delivered straight to your inbox:<\/p>\n
What is data-driven maintenance (DDM)?<\/strong><\/h3>\nData-driven maintenance is a type of predictive maintenance but goes one step further. DDM uses information from multiple sources to intelligently determine when an asset needs maintaining. This could include maintenance history, previous breakages, downtime, and even other installations of the same asset elsewhere to identify operating patterns and predict when a service should happen.<\/p>\n
In order to analyse this data, generative AI is required \u2013 something the industry as a whole has been reticent to jump on board with. And perhaps justifiably so? Budgets remain static but the expectation for FM productivity and transformation is through the roof \u2013 who can afford to implement every new gadget? But as Paul discusses with FMJ, AI can analyse data with such granularity to extract patterns and create actionable strategies that it really is a game-changer for FMs in terms of resource efficiency. And contrary to common perceptions, AI and IoT solutions are not costly and complex to implement, meaning FMs can start acting on insights quickly to drive meaningful operational gains.<\/p>\n
For more information on types of planned and predictive maintenance, as well as our AI platform Senslinc, contact us here<\/a>.<\/p>\nKeep up to date on the latest industry and technology developments: sign up to receive SWG\u2019s blog delivered straight to your inbox:<\/p>\n
Keep up to date on the latest industry and technology developments: sign up to receive SWG\u2019s blog delivered straight to your inbox:<\/p>\n
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