What is Predictive Maintenance (PdM)? Learn How Industrial IoT Enables PdM
From Preventive Maintenance to Predictive Maintenance services:
The traditional maintenance activities performed by the manufacturers or OEMs’ of industrial machines and automobiles follow preventive measures like conventional health-check and periodic servicing of the equipment.
Post installation, proportional to the gradient of time and operating conditions the preventive maintenance becomes primitive way to monitor and maintain the optimal performance of the equipment.
This is because Preventive maintenance is conducted as per the pre-defined timeline.This timeline is generally decided by the manufacturer/OEM by testing the product under ideal operating conditions.
With the advent of Industrial IoT, it is possible to stream continuous data to monitor the machine wear and tear under the prevailing operating conditions.
And hence Industry 4.0, empowered by the sensor technology, embedded software, cloud computing and big data, is now better equipped to prevent or postpone breakdowns.
An Industrial IoT based preventive maintenance has the potential of positively impacting the bottom line of business by saving downtime and repair costs.
What is Predictive Maintenance? – The definition
Predictive Maintenance is the process of condition based monitoring of an equipment or product. This type of maintenance is provided by the manufacturers based on a pre-defined set of monitoring parameters.
The continuous monitoring of the data can predict anomalies in the system that can help in instant maintenance activities thus reducing the breakdown or down-time of the equipment.
How Industrial IoT enables Predictive Maintenance?
Following is a snapshot of IoT framework that facilitates predictive maintenance:
- The network of sensors continuously collects different parameters from the machine components that are fed to the microcontroller (MCU) board.
- The MCU is connected to the wireless communication module which helps in transmission of data to the cloud server or backend database.
- The data is stored and processed in cloud server to facilitate the analysis that enables decision making at the data centers.
- Based on the statistical analysis of historic data and the behavioral analysis of the already existing data, decision-making happens for the preventive maintenance of the equipment
- There are some popular Big data analysis tools that facilitate such analysis like IBM Watson Analytics, Google Analytics, Vmware tools and more.
The Vmware tools, available in market, range from freeware like R and python to commercial tools like SAS, Tableau and Splunk.
Depicted in the below diagram is the data flow process
From reactive to proactive maintenance
The objective of predictive maintenance is to recognize system faults due to inhibited distortion of certain parameters or changed behavior.
The sensor networks and big data analysis enable operators to recognize if there is any deviation from the recommended operating range
Such predictive maintenance red-flags are calculated based on the outcomes of the present data and the past data comparison of the machine behavior.
A red-flag is triggered based on time under usage, events and meter readings. In this way a failure point is identified much prior to the actual breakdown thus delaying the entire process or avoiding unplanned maintenance or replacement cost.
The predictive maintenance helps in planned and scheduled maintenance activities. This helps in effective productivity (with minimal down-time) and maintenance.
Cost Benefits of Using Predictive Maintenance:
- Cost of replacement is reduced: the break-down chances are reduced to minimal thus extending the life-span of the system.
- Cost of labor is reduced: Planned maintenance ensures less amount of time invested on repair. It helps to avoid immediate callouts due to critical failures.
- Loss in production is reduced: The fault points are recognized and maintenance is deployed immediately thus resulting in minimum down-time and optimal productivity.
- Risk of hazards is reduced: On an industry production floor if the equipment deviates from normal operating conditions then with predictive maintenance in picture the anomaly can be detected immediately. This leads to safer working conditions for employees and can prevent any serious hazard.
Challenges of implementing Predictive Maintenance:
Following are some of the challenges an organization may face during migration to Predictive Maintenance ecosystem
- Development and integration of new software platforms with existing systems.
- Specialized embedded software skills are also required for support and maintenance of such advanced platforms
- Substantial number of hours are required for sufficient training of the existing workforce. In certain cases, a need may arise to hire additional staff
- The biggest challenge in migrating to Predictive Maintenance is the need for change in organization culture and legacy processes
Why are businesses migrating to Predictive Maintenance
Despite some challenges, why are organizations and businesses still investing in Predictive Maintenance?
Following numbers have all the answers
- According to the survey conducted by Schneider Electric in The United States on Predictive maintenance, industries can save 8% to 12% of the total revenue due to reduction of chances of equipment breakdown by 70-75%.
- The survey suggests that predictive maintenance can lead to 25-35% deduction in maintenance costs thus booming the ROI by 10 times.
- The exemplary reduction of down-time to about 35-45% motivates the industries to adopt predictive maintenance as the primary form of maintenance and monitoring technique.
- The quintessential result of so much cost and time saving generates 20-25% more production.