How Predictive Maintenance can Help Manufacturing Industry
Industry 4.0 is the new revolution which has changed the manufacturing industry. The manufacturing industry has started using artificial intelligence and industrial Internet of Things technology to boost their production and at the same time keep a check on their production equipments. Machine learning techniques have ensured that very less human intervention is needed for production. This new initiative has brought smart working environments where companies can make optimum use of their available resources. The equipments also need to be better equipped with smart technologies in order to achieve the desired level of automation required for a manufacturing company.
Predictive maintenance can offer innovative solutions to companies to achieve their desired target. Companies want the manufacturing unit to run at optimal speed at minimum downtime. Machines with moving parts require maintenance at regular intervals to prevent it from any wear and tear. There are two traditional ways to avoid unwanted breakdowns. They are as follows:
Fixed Maintenance: Companies schedule fixed maintenance of their equipments at fixed time intervals regardless of the condition of the machine. This approach is precautionary and sometimes can induce unnecessary expense.
Condition-Based Maintenance: It is a smarter way to maintain a piece of equipment as condition-based maintenance is done only when a machine requires maintenance. Maintenance is performed before a failure.
Predictive maintenance is the best approach as it can predict if a machine requires maintenance well in advance before a system failure. Predictive maintenance with condition monitoring and analysis of IoT data, can predict when a machine requires maintenance. Predictive maintenance can improve technical support drastically by catching the errors that can escape the human eye like vibrations and sound emissions. The new cutting-edge technologies like big data analytics can monitor large volumes of data in real time. The data is fed through low-power sensing devices which are installed directly on machines. These smart sensors consist of power nodes and microcontrollers which offer many advantages over the traditional condition-based maintenance.