Predictive maintenance is a set of activities the aim of which is to predict when an equipment failure might occur with a goal of reducing equipment downtime and equipment breakdown.
While traditional predictive maintenance has limited functionality, new technological developments have propelled the industry forward by introducing AI and machine learning into the picture. By implementing modern machine learning solutions, production facilities, like conveyor belt lines, can be monitored automatically without human involvement while detecting small changes in equipment operations.
Machine learning models are typically used to detect events on a video coming from surveillance cameras installed inside the facility. ML models are capable of detecting events in real time, making them an indispensable tool for modern predictive maintenance systems.
Conveyor belt lines are prone to breakdowns, so many manufacturing facilities are often equipped with monitoring sensors, surveillance cameras, and other tools for continuous monitoring of the conveyor line. Idle time is costly, so conveyor belt owners are always looking for ways to minimize breakdowns and ensure stable operations, introducing new and more advanced tools for equipment monitoring.
Here are some of the things that can go wrong with a conveyor belt during its operation:
Belt Misalignment: When a conveyor belt is not properly aligned, it can cause tracking issues, leading to the belt running off its intended path. This can result in uneven wear, damage to the belt, and increased friction, which can stress the system's components.
Belt Slippage: Belt slippage occurs when the conveyor belt loses traction with the drive pulley. This can be caused by various factors, including inadequate tension, excessive load, or a damaged belt surface. Slippage reduces the conveyor's efficiency and may lead to material spillage.
Material Spillage: Material spillage happens when items being transported on the conveyor fall off or are ejected from the belt due to various reasons, such as misalignment, excessive speed, or overloading. Spillage can create safety hazards, increase cleanup efforts, and disrupt workflow.
Belt Wear and Tear: Over time, conveyor belts can experience wear and tear, leading to cracks, cuts, and thinning of the belt's surface. These issues can weaken the belt and reduce its lifespan. If not addressed, worn belts may eventually fail.
Belt Tear or Breakage: Sudden tears or complete breakage of the conveyor belt can occur due to excessive stress, sharp objects, or manufacturing defects. Such failures can halt production and require immediate replacement or repair.
Roller and Pulley Problems: Rollers and pulleys are essential components of conveyor systems. Malfunctions such as seized or misaligned rollers, damaged pulleys, or worn-out bearings can disrupt the belt's movement and lead to conveyor stoppages.
Conveyor Blockages: Material blockages or jams can occur at various points along the conveyor path, particularly at transfer points and in incline sections. Blockages can overload the system, strain motors, and damage components.
Motor and Drive System Failures: The motors and drive systems that power the conveyor can experience issues like motor burnouts, electrical faults, or gearbox failures, which can result in complete or partial conveyor downtime.
Sensor and Control Problems: Conveyor systems often rely on sensors and control systems for automation and safety. Malfunctions in these components can lead to inaccurate tracking, improper speed control, or emergency shutdowns.
Structural Damage: Conveyor frames and support structures may suffer damage due to accidents, impacts, or excessive loads. Structural issues can compromise the conveyor's stability and functionality.
Lubrication and Maintenance Neglect: Failure to perform regular maintenance tasks, such as lubricating bearings and checking for loose fasteners, can result in premature wear and component failures.
Environmental Factors: Environmental conditions like extreme temperatures, humidity, dust, and corrosive substances can accelerate wear and tear on conveyor components.
A conveyor belt monitoring system is a specialized technology and set of tools designed to track and manage the condition, performance, and safety of conveyor belts and their components in industrial settings.
These systems use various sensors, software, and communication networks to continuously gather data, analyze it in real-time or through periodic assessments, and provide valuable information to operators and maintenance personnel. The primary goals of a conveyor belt monitoring system are to enhance operational efficiency, reduce downtime, improve safety, and extend the lifespan of conveyor equipment.
By implementing a conveyor belt monitoring system, industrial facilities can proactively manage conveyor operations, reduce maintenance costs, prevent unplanned downtime, and ensure the safe and efficient transportation of materials and goods within their production processes. These systems are especially valuable in industries where conveyor belts play a critical role, such as manufacturing, mining, logistics, and distribution.
Implementing a conveyor belt condition monitoring system offers several advantages to industrial facilities and operations. These systems help improve efficiency, reduce downtime, enhance safety, and ultimately save costs.
An increase in machine lifetime is one of the significant advantages of implementing a conveyor belt monitoring system in industrial facilities. This benefit is closely tied to the system's ability to provide proactive maintenance insights and condition-based monitoring.
Conveyor belt monitoring systems continuously collect data from sensors placed throughout the conveyor system, including information about belt tension, alignment, speed, and component condition. By analyzing this data, the system can detect early signs of wear, misalignment, or other issues that could lead to equipment breakdown.
Conveyor belt breakdowns can be costly and result in extended downtime. A monitoring system's ability to detect anomalies and emerging issues allows for proactive intervention, preventing catastrophic failures that could severely damage the conveyor equipment.
Rather than replacing conveyor components based on a fixed schedule or waiting for them to fail, monitoring systems enable condition-based component replacement. This means that parts are replaced when they reach a certain wear threshold, maximizing their usable lifespan.
Instead of performing maintenance on a fixed schedule, maintenance teams can rely on the actual condition of conveyor components. Replacements or repairs are carried out when the system indicates that a component has reached a predefined wear or damage threshold, reducing unnecessary and premature maintenance.
Maintenance teams can allocate resources, including labor and spare parts, more efficiently. They can focus their efforts on addressing identified problems rather than performing routine maintenance tasks that may not be needed at that time. With scheduled maintenance and less frequent breakdowns, labor costs associated with maintenance activities are reduced. Maintenance personnel can work more efficiently, focusing on tasks with a clear purpose.
Many conveyor belt monitoring systems are equipped with emergency stop controls that allow operators to halt the conveyor's operation immediately in case of safety hazards, accidents, or equipment malfunctions. This quick response capability can prevent accidents and injuries.
Workers can monitor the conveyor's status and performance in real-time through user-friendly interfaces. This visibility enables them to identify potential hazards and address them proactively.
Additionally, monitoring systems can be used as training tools to educate workers about safe operation and maintenance practices. They can provide visualizations of potential hazards and safety procedures, enhancing worker knowledge and awareness.
Monitoring systems use historical data and predictive algorithms to forecast when maintenance tasks will be needed. This enables maintenance teams to schedule downtime during planned maintenance windows. Planned maintenance based on predictive insights reduces the frequency and duration of maintenance shutdowns, minimizing their impact on productivity.
We create AI software - and we do it well. Talk to us to get your project started today
Computer vision plays a vital role in predictive maintenance by providing a powerful set of tools and technologies to monitor, analyze, and predict the condition of industrial equipment and assets.
One key application of computer vision in predictive maintenance is the continuous monitoring of equipment and assets through visual data. Cameras and sensors capture images and videos of machinery, allowing for real-time analysis of their condition and performance.
Computer vision algorithms can then detect anomalies, such as unusual wear patterns, corrosion, leaks, or structural damage, by comparing the collected visual data to historical records. These anomalies are early warning signs that indicate potential equipment failures, enabling maintenance teams to intervene proactively before costly breakdowns occur.
By leveraging historical data and current visual information, machine learning models can predict when maintenance is likely to be required. For example, if a computer vision system detects excessive wear on a conveyor belt or abnormal temperature patterns in a motor, predictive maintenance models can use this information to forecast the remaining useful life of these components. Maintenance schedules can then be optimized, minimizing downtime and reducing the overall cost of maintenance activities.
Computer vision facilitates remote monitoring and condition assessment of equipment, which is particularly valuable in industries with distributed or hard-to-reach assets. Maintenance personnel can remotely access visual data and receive alerts when anomalies are detected, allowing them to assess the situation and plan maintenance actions without physically being present at the equipment location.
This not only improves safety by reducing exposure to hazardous conditions but also ensures that maintenance resources are deployed efficiently, contributing to increased equipment reliability and operational productivity.
High-resolution cameras are strategically placed along the conveyor system to capture visual data. These cameras record the conveyor belt's movement, the materials being transported, and the surrounding environment. The collected images or video frames serve as input data for the computer vision system.
Advanced computer vision algorithms are employed to analyze the visual data in real-time. These algorithms are trained to recognize normal operating conditions and patterns. Anomalies, which can include various issues such as damage to the belt, misalignment, foreign objects, or irregular material flow, are identified based on deviations from expected patterns.
When the computer vision system detects an anomaly, it generates an immediate alert. These alerts can be sent to maintenance personnel, supervisors, or integrated into the plant's control system. The alert specifies the location and nature of the anomaly, allowing for quick response and intervention.
Over time, the system accumulates a wealth of historical data on conveyor belt conditions and anomalies. This data can be used to develop predictive maintenance models. By analyzing patterns and trends in the data, organizations can forecast when maintenance is likely to be required, enabling proactive maintenance scheduling and minimizing unexpected downtime.
Often, manufacturing facilities have a need to track items transported by conveyor belts to assess their quality.
The tracking performed by surveillance cameras in real-time allows for precise monitoring of an object's position, speed, and trajectory. It enables the system to follow an object's journey from the beginning of the conveyor to its final destination or transfer point.
Object recognition and tracking can be used for quality control purposes. The system can compare detected objects to predefined standards or specifications, identifying defects, anomalies, or missing components. Based on this analysis, the system can trigger sorting mechanisms or reject non-conforming items from the production line, ensuring that only high-quality products are delivered to customers.
Computer vision systems can improve the accuracy and consistency of quality control, reducing human errors and variability.
Computer vision algorithms analyze the visual data in real-time or near-real-time to identify defects, irregularities, or other quality issues in the items being conveyed. These algorithms are trained to recognize specific features, patterns, or defects that may indicate non-conformity with quality standards.
Computer vision can identify a wide range of defects, such as scratches, cracks, misalignment, missing components, color variations, or size deviations. It can also assess other quality attributes, including surface finish, print quality, or assembly integrity.
The system logs data on inspection results, including the type and location of defects. Historical data can be used for process improvement, root cause analysis, and trend analysis to continuously enhance product quality.
Using computer vision for predictive analytics on a conveyor belt is a valuable solution for industries seeking to optimize maintenance practices, reduce downtime, and enhance operational efficiency. By harnessing the power of visual data and advanced analytics, organizations can transition to data-driven maintenance strategies that prevent equipment failures and increase the reliability of conveyor systems.
Machine learning models can predict when specific maintenance tasks, such as belt replacements or component inspections, should be scheduled. Predictive analytics also consider factors like environmental conditions, conveyor load, and equipment runtime.
When potential issues or anomalies are detected, the system generates alerts and maintenance recommendations. Maintenance teams receive these alerts, specifying the location and nature of the issue, allowing for proactive maintenance planning and execution.
The use of predictive analytics enables organizations to transition from reactive maintenance practices to proactive ones. By addressing maintenance needs before they lead to equipment failures, organizations can minimize unexpected downtime and production disruptions.
At Businessware Technologies, we focus on developing cutting-edge AI solutions for real time object detection and recognition. We have had an opportunity to work on a computer vision system for a bottling facility, the main goal of which was to enhance the predictive maintenance efforts.
The system helps to identify improperly canned bottles and low filling lines, reducing the number of defective bottles and improving customer satisfaction by providing consistent quality of products. The system is more efficient than daily checkups of random bottles as it detects every faulty bottle, eliminating the need to throw out a whole batch.
Beverage bottling is a long process consisting of a series of sequential actions with each following action depending on the quality of the last one. If something goes wrong at any step of the process, not only the faulty bottle needs to be discarded, but it can affect the entire batch. For example, if a bottle is not properly sealed at a canning station, the overflowing beverage will interfere with the labeling process, resulting in either unreadable or unlabeled bottles.
Bottling line sampling, or hourly inspections of randomly picked bottles, is not effective nor efficient. It takes a significant amount of time to pick the bottles among dozens of containers, assess fill height, label placement, cork height, etc. Faulty bottles can easily slip by the inspector's eye, decreasing user satisfaction and resulting in a monetary loss for the company.
Our client installed security cameras over the conveyor belt and has granted us access to the video feed to process it using a machine learning model. We have collected images from the security cameras to create a dataset for the model to train on.
The ML model trained on the dataset is capable of detecting various events at different stations in real-time. The system monitors three conveyor belt stations — filler, canning, and labeling.
The system can not only be used to detect faulty bottles but to assess the condition of equipment, detecting early signs of a breakdown. For example, bottles are usually filled low due to a degraded or broken water seal, which can lead to a complete production shutdown, causing significant monetary losses. Our system can detect these issues early on and let the client know the equipment needs to be inspected.
The system can also detect early signs of malfunction based on abnormal equipment moving patterns, increased vibrations, dirt or rust.
Daily and monthly reports help to diagnose whether the number of faulty bottles increases over time and which stations produce the most rejects. The client can easily access these metrics through a user-friendly dashboard both on mobile and desktop.
Businessware Technologies is a reliable computer vision development vendor: we have been recognised as one of the top software development companies by Clutch and Manifest, it is a Top Rated Plus agency Upwork, and has received local awards for its excellent work
A team of over 70 highly skilled software engineers with extensive experience in developing complex software for both startups and Fortune 500 companies
Deep expertise in modern computer vision technologies and approaches to system development, like data science, AI and machine learning, OpenCV, Python, Tesseract, and many more
Businessware Technologies is a Microsoft Gold Certified partner
Businessware Technologies is compliant with GDPR, ISO 9001, ISO 27001 standards
Businessware Technologies works with Fortune 500 companies and has had decades-long relationships with most of its clients
Businessware Technologies has proven to be a reliable computer vision outsourcing partner by having an excellent track record in computer vision development backed by an extensive portfolio of successful projects.
If you have a computer vision project in mind and need help with implementation, contact our manager and they will be happy to help you.