The Future of Maintenance

Author: Sanjeev Kumar


Many industries can confirm how maintenance costs represent a large proportion of operating costs. Figures can vary from one company to another but can easily still represent up to almost 45% of the global production costs, all without taking into account planned or unplanned downtime, stock and tools management and finally purchasing.


These are as many unforeseen events which can lead to additional costs that a company often struggles to estimate but that are nonetheless closely linked to the engineering maintenance teams’ activity. It strongly goes without saying that an organisation's profitability and productivity partly depend on the maintenance processes that have been implemented; sites therefore should accommodate a well-thought and optimised strategy in order to make sure all equipment run in the most reliable and efficient way needed.


If maintenance technicians and managers are given the chance to check on their equipment’s conditional status and to enter information in a tool that is helping them to anticipate potential breakdowns, they can become way more efficient and perform well-organised interventions. The final aim is to reduce a whole sites’s useless spending's as well as to increase productivity and profitability.



What is Predictive Maintenance?


Predictive maintenance (PdM) is defined as the type of condition-based maintenance that monitors the condition of mechanical equipment using sensor devices. The sensor devices supply data in real-time, stored in a secure, cloud-based network that you can access at any time which is then used to predict when the asset will require maintenance and prevent equipment failure and costly damages. This information is then Data from the sensor can be used to predict when you should perform proactive maintenance or preventative maintenance.


Other types of Maintenance


Reactive Maintenance


Also known as breakdown maintenance and run-to-failure, essentially a strategy where maintenance work is performed only after a breakdown or failure takes place. Whilst it needs minimal planning there are many drawbacks to this method with high possibilities of unscheduled downtime, little to no insight and a potential 'ripple' effect where other equipment down the production line malfunction which can also lead to high costs associated with parts and suppliers.


Planned Preventive Maintenance (PPM)


This routine based method includes periodically taking assets offline and repairing/ inspecting them at set intervals (based off event or time-based triggers) and has a goal of keeping equipment up and running. Although improving asset lifespan and reliability - it doesn't take into account asset wear which means you will be doing excessive maintenance on those assets. This method is also more labour intensive with more frequent checks leading to higher costs to run over time.



The Solution: Foundations® Connect


The first step in implementing predictive maintenance through Foundations® Connect is installing sensors and a gateway to your mechanical equipment which is non-intrusive and connects within a matter of seconds. Now live, when you begin to collect conditional data you will be able to benchmark and establish limits and henceforth there is a “control” to compare any abnormalities to. From there, it’s simple - any time a piece of equipment performs outside of normal parameters, the sensors trigger your predictive maintenance protocol which can be customised to your efficiency. A work order is generated in Foundations® CMMS (Computerised Maintenance Management System) and assigned to technicians so they can perform any required repairs to address the anomaly before it becomes an issue.



As shown above, the development of typical machine failure where Foundations® Connect can intercept and spot signs of wear, a reduction in performance, and anything out of the ordinary early in the maintenance prevention window shown, with built in intelligence the IoT (Internet of Things) devices can 'learn' about equipment condition to generate limits for alert notifications via text or email and monitor 24/7 without complaints.


Examples of different types of Sensors


Vibration Sensor:

Vibration Sensors use an accelerometer to measure g-force on 3 axis and then determine speed and frequency applied. It can detect most ordinary faults including looseness, imbalance, misalignment and late-stage bearing wear which can help indicate when further analysis is required to decide when action such as re-lubrication, replacements or repairs are needed.


Temperature Sensor:

Temperature Sensors use a thermistor to accurately measure temperature fluctuation that usually indicates machine condition or environmental problems that may affect a machine performance through over-heating and lead to failure. These sensors are prime for monitoring ambient temperatures around the sensors physical location.


Humidity Sensor:

Humidity Sensors allow you to monitor the relative humidity of the air within a room or enclosure. In industries like refineries, metal, or other industries in which furnaces are used, high humidity will reduce the oxygen in the air and henceforth reduce the firing rate. Other industries like food processing, paper, textile are vital for control of humidity.


Sensor and Hub Design


The superior wireless range of the Foundations® Connect sensors results in the need for fewer hubs and lower running costs. The sensors are designed to run on minimal power and can operate for several years on 2 AA Batteries.



The Connect Wireless Hub can be used to communicate with up to 100 sensors and connects directly with the CHS Foundations® via the cloud. The fact that there is no wiring or external power requirements results in rapid deployment without any impact on the existing IT Infrastructure. Additional sensors can be added to the wireless network at will with minimal setup effort. Additionally, the hub can be used to interface with other sensors that are attached to diverse items of equipment throughout the same building.


Example Situation


A large parcel company owns a shoe sorter that uses sliding shoes to move parcels off the sorter and down specific chutes to the correct delivery bays. The sorter consists of aluminium slats that are chain driven and powered by an MGU. This is critical kit as there is often no contingency to sort the parcels if the sorter fails. Parcels would be collected and sorted manually, which could keep lorries waiting impacting delivery and throughput.


CHS Services used Foundations® Connect to install vibration monitors located on the bearing of the drive end of each sorter on either side of the track. The installation setup picked up problems throughout the length of the sorter. The under-slung MGU was also monitored for vibration and temperature, all using the Gateway 2 Foundations® Connect Hubs.


The client received an alert and early warning data analysis from the CHS team showing an increase in vibration. The client carried out an inspection in planned downtime and found that part of machine was damaged and rubbing along the edge of track.


Their engineering team rectified the fault before any severe damage to the sorter was caused.


Finding the fault early also avoided excess damage and having to carry out reactive repairs during a busy time of day when the equipment is in operation.


Benefits of Predictive Maintenance


This forward thinking method creates 'smart assets', optimising maintenance cycles, predicting and preventing failures, informing operations in real-time and predicting asset life-cycles and thus:


  • Improving welfare and safety

  • Saving time and money

  • Improving labour efficiency

  • Streamlining maintenance costs through reduced labour, equipment, and inventory costs.


The ROI of Predictive Maintenance


Directly from the 'Plant Engineer's Handbook' the following applies to Predictive Maintenance and business's we've worked with in the past:


  • Cut maintenance costs by 50%

  • Reduce unexpected failures by 55%

  • Cut repair/overhaul time by 60%

  • Reduce spare parts inventory by 30%

  • 30% increase in machinery mean time

  • 30% increase in up-time


Conclusion


Predictive maintenance through the next wave of business efficiency and IoT sensors seeks to establish the peak time to perform work on an asset so maintenance frequency is decreased and reliability is increased without the unnecessary costs.


Whilst associated with high costs, Foundations® Connect offers something that's value for money and can easily provide an ROI (an increase from 20% to 35% based on the first two years) that turns the maintenance department into a source of cost-savings and higher profits.


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