Internet of Asset Maintenance
Internet of Things deals with data exchange over internet between uniquely identifiable objects. It also encompasses the Machine to Machine (M2M) communication to achieve this. Some of the best examples of usage of IoT are monitoring health conditions of heart patients with pacemakers and prescribe proper medication, predict weather conditions and take necessary precautions to alert people, reduce energy consumption of Air conditioners by adjusting the temperature based on room conditions, Inform the train passengers on the nearing station names, track your lost equipment / mobiles on maps, etc.
IoT is dependent on the following elements to make it work:
• A way to uniquely identify an object - such as a RFID
• A means to sense the changes happening in and around the object - using sensors
• A medium to communicate these changes to external world - Internet - either wired or wireless
• A receiver to interpret these messages, save them in database in a recognizable format - by data transformation through interfaces
• A business intelligence to analyze the collected data, provide useful insights and enable decision making / do some automatic actions - using an analytics software
When we look at the list of elements in IoT, one can very well relate them to the latest philosophy of Predictive Maintenance being implemented by various asset intensive organizations, which is a paradigm shift in the way we do the maintenance activities in recent times. Organizations are moving ahead from corrective to preventive to the much efficient predictive maintenance approach. Predictive Maintenance provides the advantage of reduced downtime, effective usage of resources and cost savings since the maintenance is done only on need basis based on predictions.
Predictive Maintenance revolves around the concept of Condition Based Monitoring / Reliability Centered Maintenance which are primarily focused on collection of real time data from the assets, analyzing the data and carry out maintenance on the basis of outcomes. Some of the common conditions monitored are temperature, pressure, fluid flow, vibration, overload etc. This is achieved using sensors or similar devices to sense the condition of the asset and communicate the information to a system. Hence, it pretty much already into the arena of IoT and M2M.
However, few interesting value adds from IoT to Predictive Maintenance are:
1. Cost Savings on Initial Investments: Organizations are making huge investments to establish the setup for sensing the condition, collect and transform the data and do predictive analytics. IoT provides the advantages of enabling some of these functions in cloud platforms thus reducing infrastructure cost. Also, most of the existing systems are using data transfer between the assets and systems through analog signals. IoT is enabling the digitization of assets where the data transfer is handled using Smart devices and high speed internet thus reducing the investment on analog interpreters.
2. Enhanced Automation: There are human interventions in taking the maintenance decisions based on a predicted outcome, specifically in distributed asset environment. IoT is helping in enhancing the response to a prediction and implement automatic preventive measures on the assets by themselves which can be called as Self Fixing.
3. Efficient Data Processing: Managing high volume of data collected from various systems and analyzing them using an analytics software becomes cumbersome. New inventions on IoT are coming up where the data transformation, segregation and filtering are done at the asset side itself. Only processed, meaningful data is identified and communicated to the analytics system.
4. Effective Asset Monitoring by OEMs: OEMs are trying to enhance their services to the customers by monitoring their equipment conditions and proactively provide solutions. IoT helps in connecting various equipment to the OEM database thus helping the manufacturer to track the performance of the equipment, provide proactive solution, draw design decisions and reduce equipment downtime, eventually improving customer satisfaction. For example, a way for the manufacturer to find an issue in your car and fix it remotely thus reducing your time to visit the service station.
With IoT adding value to the way predictive maintenance is done, we can very well visualize its application in few of the below examples:
• Do a self-fixing inside the equipment whenever there is a problem. If there is an increase in temperature of the bearings due to lack of lubrication, the asset itself should have a lubricator which can automatically lubricate the bearings at that instance, thus avoiding any wear and tear of the bearing.
• If the fluid flow is found reduced due to a filter clogging, system should find the alternate filter and reroute the fluid flow through the alternate filter. Thus the clogged filter can be removed from the equipment even without shutting it down.
• Make the changes in the equipment through smart devices to vary the operating parameters through artificial intelligence so that the equipment can run without any stoppage during extreme conditions.
• Understand the problem by sitting in a control room and enable fixes remotely. Reduce the field visits.
Finally, the answer to our initial question is: Internet of Things encompasses the Predictive Maintenance philosophy which is already in use for asset maintenance. IoT extends the effectiveness of Predictive Maintenance by providing better connectivity between assets and systems using ever evolving Internet technology and smart network. It makes the condition monitoring seamless and makes the decision making much more efficient. Thus we can say that exploring the horizons of IoT for asset maintenance business functions can truly get it transformed as 'Internet of Asset Maintenance'.