The optimization of asset life cycle and reducing the maintenance costs are two important daily concerns of asset managers. The asset downtime can keep everyone awake at night and working over the weekends. Machinery parts wear out and its components fail. When these things happen, fixing them is a struggle while production has stopped and needs to be restored as soon as possible.
Preventive maintenance (PM) is an old-fashioned way to handle machinery failures ahead of time. In this approach, machines and equipment are shut down during specific time windows determined by maintenance and operations engineers. PM activities can be set based on either calendar or usage. But, these days, PM is not considered the best practice as it wastes resources by asking for costly inspections and maintenance actions when they aren’t necessary. Besides, it doesn’t completely prevent failures.
Predictive maintenance aka condition based maintenance is another strategy that has superseded preventative maintenance in many industries. Vibration monitoring, lubricant analysis, thermography inspection and other condition monitoring tools are used to detect faults in machinery and fix them before they result in more serious problems. These techniques usually need high expertise in the plant to analyze data, detect faults and diagnose problems.

Prescriptive maintenance, which uses statistical models, data analytics, and forecast techniques offers advantages both over preventive maintenance and predictive maintenance. These days, the Internet of Things (IoT) and predictive analytics are playing an important role to improve operations and asset management outcomes. In other words, when data and analytics are combined with connected assets, systems, and platforms, the outcome can considerably impact asset reliability and uptime.
Performing maintenance just in time is a key target for any asset management initiative. This can be achieved through predicting failures by measuring and analyzing the machinery condition in real time and prescribing the right action at the exact moment that is needed. This is the motto of asset management: fix it just when it needs to be fixed.
Prescriptive maintenance is equipped with the capabilities of all other previous maintenance strategies to optimize asset performance. Within prescriptive maintenance initiatives, machines serve as proactive role players in their own maintenance. Different technologies including digitization, IoT, Expert Systems, AI, and Big Data analytics are helping prescriptive maintenance to be superior compared to the previous maintenance management approaches.
Predicta4 combines sensor data, cloud databases, and real-time analytics and uses an enhanced alert system to support on-time decision-making and workflow management. Now, Predicta4 IoT-led continuous machinery monitoring collects sensor data from a variety of rotating machinery, combining this data with other event or reference information. Predicta4 WatchDog is equipped with an expert system and AI algorithms. It would recommend the right actions towards the best outcomes, enabling asset managers and reliability engineers to prevent failures at precise times.
Consider this Predicta4 success story:

Ray, the maintenance manager of a power plant is tired of their ID Fans’ old vibration monitoring system that only warns them when a problem has occurred and does not provide any trend nor diagnostic information. He decides to install Predicta4 vibration monitoring platform to overcome this limitation. As P-Cube is a triaxial vibration sensor, they install a P-Cube per fan bearing housing and set them up using an Easy Setup App. A few months later, the Predicta4 WatchDog system alerts them that an upward trend is happening for one of the ID Fans. It also provides some diagnostic information stating that the vibration is caused by the impeller unbalance. A work order is issued and at the right time the maintenance team inspect the impeller and find out that some material build-ups are formed on the impeller. After washing the Impeller, they notice that the vibration trend has returned to normal state.
How did Ray achieve this? With real-time vibration monitoring capability through smart wireless vibration sensors combined with an early warning Prescriptive Maintenance approach to detect, diagnose, and alert such issues on time.
By combining IoT Sensor Data, WatchDog, Data Analytics and Work-Flow Management, we can prescribe required maintenance before failures occur.
Prescriptive maintenance is a new era in asset management enabled by advanced technologies. With current data collection and data analytics capabilities, we can compile statistics on failure modes, failure rates, usage patterns, and conditions for any rotating machinery. Patterns and trends can help us develop an effective maintenance plan based on actual machine conditions rather than on some static metric. This will result in a strategic maintenance system. The future of asset management belongs to prescriptive approaches, and each step towards this goal helps industries reduce costs, optimize equipment reliability and uptime. Last but not least, this approach guarantees that all machinery is working safely and in an environment-friendly manner.