Machines have become the best friends of humans in this highly technological era. Today human life would be imaginable without machines. From inventing the wheel to developing multiple rockets, making has come a long way in developing and dealing with machines.
With the advent of revolutionary technologies like artificial intelligence, machine learning, data analytics, etc., the scope, application, and influence of devices have been increased tremendously. Today, machines are expected to perform tasks programmed into them and make decisions and come up with new solutions.
Machines have acquired decision making prowess in specific fields, and as such, we humans depend upon to design not only new models but also revisit old concepts.
Machine Learning and IIOT in Industrial Malfunctioning
One denomination common for all machines is that they break down or start malfunctioning. This may be due to a plethora of reasons but ultimately lead to huge losses.
To avoid this, predictive maintenance based on IoT (Internet of Things) and Machine Learning can be reduced. This would lead to better efficiency and productivity and reduce the burden on the machines as a whole.
Industrial Internet of Things or IIOT, in layman terms, can be defined as a system of interconnected devices utilized in industries. The devices can transfer data and findings in their immediate surrounding to all the devices connected with them in real-time.
Thus in a given network of methods and apparatus, data can be transferred over multiple layers without any human intervention. This ensures that there is seamless connectivity and transparency within the given network.
With advancements in computer science, the network, and the devices in the IoT bubble have expanded, for a complex system like manufacturing plants, oil refineries, etc., seamless connectivity can go a long way in ensuring predictive maintenance.
As all the components are connected, defects or malfunctioning in one segment can be detected in real-time. Once the defected portion is exposed using IoT, the same can be isolated and attended to individually without hampering the entire chain of processes.
Furthermore, each part of the machine has its threshold for optimum functioning. Since all the pieces are interdependent, any change in one component severely affects the limit for other regions. Using IIoT, each part’s performance can be tracked individually on a real-time basis, thus leading to segmentation and compartmentalization.
This is akin to breaking a more significant problem into smaller parts and then solving them independently. This would save not only time but also reduce costs.
Manpower Planning with Machine Learning and IIoT
IoT reduces the need for human intervention; thus, the scope of human error and social bias is also reduced. This leads to better predictive maintenance as the decisions are made in a more objective and data-driven manner.
Besides IoT, machine learning is another concept that can be used to bolster predictive maintenance in machines and equipment. Machine learning can be defined as the concept of designing algorithms that can make an informed decision for a given data set based on past experiences.
With the exponential increase in computing prowess, machine learning models have become more accurate in predicting future outcomes.
So by using machine learning, smarter and self –correcting equipment and machine parts can be built. Thus when there is any anomaly in functioning, which can lead to equipment failure, it can be detected beforehand and dealt with proportionately.
In the same vein, the individual or, in some cases, the critical components of equipment can correct themselves automatically without any human intervention. This would be a quantum leap in predictive analytics to prevent equipment failures.
The combined application of machine learning and IoT could raise the bar as far as predictive maintenance is concerned and lead to negligible equipment failures.
Coupling the IoT application of transferring data over real-time over the entire network with the self-correcting and judicious use of machine learning, the whole system can be placed under the umbrella of predictive maintenance, thus reducing equipment failures.
Optimizing Productivity with Machine Learning and IIoT
Since the entire network is connected, any malfunctioning in one interface detected and corrected by a machine learning algorithm would be easily replicated and transmitted to a different section of the equipment. So, in essence, the entire equipment is better able to analyze the input parameters and hence work optimally.
Since most equipment failures occur when the equipment is unable to work at the threshold level, machine learning can ensure that each section works at optimal levels. The same information will be transmitted across the entire network to ensure that the whole apparatus and the equipment are working at an optimum level.
Equipment failures are devastating as they lead to unforeseen losses resulting in massive revenue losses for any firm. With the use of IoT and machine learning, giant strides in predictive maintenance can be taken, which would reduce equipment failures to a high level.