What’s behind the operation of industries?
It may seem like a play on words, but at the core, it’s about ensuring that machines work.
Two main factors affect their performance: the efficiency of personnel and maintenance, both routine and extraordinary. Within these two broad maintenance categories, different types vary based on the causes requiring intervention.
In summary, routine maintenance involves all diagnostic activities on a machine, such as inspections. On the other hand, extraordinary maintenance activities refer to corrective actions following sudden failures or technology upgrades.
Predictive maintenance utilizes data collection and analysis from machines to predict when a system will require maintenance. It allows companies to intervene in a timely and preventive manner, avoiding costly production shutdowns and urgent repairs.
It involves monitoring the health status of the machinery to prevent prolonged downtime that can be burdensome for companies. That’s why it falls under preventive maintenance.
Differences between Preventive Maintenance and Predictive Maintenance
Just as preventive maintenance falls under routine maintenance, predictive maintenance falls under preventive one, aiming to prevent specific errors or malfunctions. Unlike other preventive interventions such as inspections and predetermined periodic checks, predictive maintenance aims to prevent critical issues through targeted inspections with measurements, readings, and verifications. Real-time analysis of asset conditions uses tools and instruments capable of detecting faults and anomalies in advance, allowing for timely corrective actions to avoid operational interruptions.
Predictive maintenance goes beyond prevention and aims to predict the future probability of failure using Industry 4.0 technologies.
One of the commonly used Industry 4.0 technologies for this purpose is the installation of IoT sensors that constantly monitor and transfer valuable data for essential maintenance predictions.
Advantages of Predictive Maintenance
One of the main advantages of predictive maintenance is the ability to intervene promptly on machinery, reducing the time and costs of individual interventions and avoiding unnecessary maintenance operations.
As mentioned earlier, predictive maintenance prevents failures before they occur, thus reducing the costs associated with urgent equipment repairs. The timely intervention also prevents the problem from worsening, preserving the machine’s lifespan and reducing long-term costs.
Improved efficiency and productivity
Eliminating unforeseen downtime increases productivity and allows for better planning of business activities.
Reduced workplace incidents
By identifying issues with machinery promptly, predictive maintenance reduces the risk of workplace accidents.
Increased customer confidence
Avoiding machine downtime, ensuring punctuality, and defect-free production increase customer confidence and foster long-term working relationships.
Improved energy efficiency
Predictive maintenance enables tracking energy waste and inefficiencies, reducing energy costs and, most importantly, the company’s environmental impact.
Implementing Predictive Maintenance
Predictive maintenance is made possible through machine learning. Generated data by intelligent sensors (IoT) on the machinery can be used to train machine learning algorithms according to specific needs. This way, predictive maintenance can be scheduled promptly, avoiding sudden failures and costly corrective interventions.
IoT sensors are devices that constantly monitor the condition of company assets and collect real-time data. Data is processed to identify anomalies or degradation trends, allowing for the scheduling of maintenance interventions.
Examples of Predictive Maintenance
This technology detects anomalies in asset vibrations, which could indicate the presence of failures. Tools used for vibration analysis include accelerometers, microphones, and spectrum analyzers.
This technology detects anomalies in asset temperatures, which could indicate the presence of failures or malfunctions. Tools used for thermographic analysis include thermal cameras and infrared thermometers.
This technology evaluates the quality of lubricating oil and detects contamination or the presence of metal particles, indicating ongoing failures. Tools used for oil analysis include spectrophotometers and microscopes.
This technology monitors the quality of water used in facilities and detects contamination or the presence of harmful substances that could cause operational problems or failures. Tools used for water analysis include spectrophotometers and conductivity sensors.
Predictive Maintenance and Smart Glasses
The use of smart glasses can make predictive maintenance even more effective and accessible. They allow personnel to collect data, remotely control equipment, receive technical assistance, provide training, and use augmented reality to reduce intervention costs and improve workplace safety.
With smart glasses, it’s possible to collect data from sensors and detection devices connected to equipment. Collected data allow us to monitor equipment performance and identify maintenance issues.
Real-time technical assistance
Smart glasses enable operators to receive remote technical assistance, allowing an expert to see what the on-site personnel observe and provide real-time instructions. It helps reduce machine downtime and resolve issues more quickly.
Smart glasses train new hires and provide updates to personnel. For example, operators can view step-by-step maintenance or repair procedures through the glasses.
Smart glasses can use augmented reality to display equipment information, such as performance data or maintenance instructions, directly on the operator’s glasses. It helps identify maintenance problems and take corrective measures more quickly.
The use of augmented reality in maintenance significantly impacts industry operations and processes. It requires trust in technologies and long-term benefits for personnel, customers, profits, and the company’s image.
Contact us to explore how to integrate wearable devices, such as smart glasses, into your maintenance processes.