In today’s fast-paced business landscape, maintaining operational efficiency is crucial for staying competitive. Unplanned equipment failures and costly downtime can significantly impact productivity and profitability. This is where the power of the Internet of Things (IoT) comes into play.
By leveraging IoT-enabled predictive maintenance, businesses can proactively identify and address potential equipment issues before they escalate, optimizing performance, reducing downtime, and ultimately enhancing operational efficiency.
In this blog, we will explore how IoT-enabled predictive maintenance enhances business operational efficiency.
Table of Contents
What is Predictive Maintenance?
It is a method used to assess the state of the equipment that is currently in use and predict when maintenance needs to be done. The approach promises cost reductions as compared to time-based or routine-based preventative maintenance.
With the help of predictive maintenance, systems can be maintained and status data from machinery can be collected.
The technique uses an in-memory database, real-time analytics technology via sensors, and data analysis to pinpoint the equipment’s problem areas. The machine can then be allocated a technician before it breaks down.
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Role of IoT in Predictive Maintenance
You might be questioning why an Industrial IoT (IIoT) solution is necessary if SCADA, a control system architecture, is already in place to manage all maintenance tasks. Let’s explore that in much more detail.
- Predictive maintenance can process massive amounts of data and run complex algorithms, which local SCADA implementation cannot.
- Consider the architecture and sensor-based data gathering (e.g., supply voltage, temperature, vibration) wirelessly sent to a cloud-based storage facility for real-time insights, unlocking IoT’s full potential in predictive maintenance.
- An IoT-based software accumulates terabytes of data, leveraging machine learning algorithms to anticipate equipment malfunctions and potential dangers, empowering proactive decision-making.
- Maintenance teams extract and analyze these data sets, utilizing AI and Big Data algorithms to uncover valuable insights and identify patterns in vast volumes of data.
- To ensure effectiveness, up-to-date and precise inputs are crucial, highlighting the significance of data-tracking IoT devices in quickly responding to equipment problems.
- IoT offers modular and user-friendly solutions, such as monitoring air compressors that power various machinery functions using controllers, sensors, and power supply transmitters.
- IoT predictive maintenance systems are easily scalable and adaptable, allowing for the seamless integration of additional equipment and sensor replacements to ensure continuous data transmission.
How IoT in Predictive Maintenance Helps to Enhance Business Operations?
IoT in predictive maintenance offers the following benefits that enhance business operations. Here’s how:
Improved Operational Efficiency
IoT in predictive maintenance boosts operational effectiveness by allowing companies to anticipate maintenance requirements. Businesses may discover possible difficulties in advance, optimize maintenance schedules, and streamline operations by continuously monitoring equipment and analyzing real-time data. This proactive strategy lessens disruptions, minimizes equipment downtime, and increases output.
Reduced Downtime
IoT-based predictive maintenance helps to minimize downtime by spotting and fixing potential equipment issues before they arise. Businesses can identify early warning indications of equipment degradation by analyzing sensor data and predictive analytics, enabling prompt maintenance or repairs. With this proactive strategy, unplanned downtime is reduced, equipment reliability is increased, and smooth business operations are guaranteed.
Increased Quality Control
IoT in predictive maintenance is essential to maintain and enhance quality control. Businesses may spot anomalies, pinpoint performance bottlenecks, and swiftly implement corrective measures by continuously monitoring the operation of their equipment.
It guarantees that machinery is operating at peak efficiency, which enhances product quality, customer satisfaction, and brand reputation.
Enhanced Safety and Compliance
It refers to the improved levels of safety and adherence to regulatory compliance that result from utilizing IoT technology in predictive maintenance practices.
IoT predictive maintenance enables businesses to identify potential safety hazards, take action, and estimate problems before they impact employees. By analyzing data from many sources and using the data produced by sensors and actuators, they can move swiftly to eliminate safety hazards.
Using data over the longest periods, you can identify potentially dangerous conditions and calculate their effects on everyday operations. To always comply with laws, a proper IoT-based predictive maintenance system sets off orders to reallocate resources and keep exposure levels below the threshold values.
Reduced Maintenance Costs
Each asset has several related expenses. The overall asset ownership cost will increase considerably if you factor in the costs of an unexpected failure. Therefore, by anticipating and avoiding equipment breakdowns, businesses can save money. Improving maintenance planning can result in significant cost savings in asset-intensive industries.
IoT-based predictive maintenance forecasts asset health, equipment utilization, and potential future events using historical data from various sources, including sensors and IoT devices. It enables you to decide based on the information, such as scheduling the most effective maintenance or routine inspections. Making repairs ahead will reduce the time the equipment needs to be down for maintenance.
Increased Asset Utilization
Unplanned equipment failure downtime, production delay charges, and high maintenance and repair costs decrease profitability. IoT-based predictive maintenance promotes the more effective use of current assets by allowing for the prediction of machine breakdowns and the reduction of maintenance concerns. Whether internal or external, it can assist in identifying the delays’ causes and set up procedures to address them.
Additionally, you may increase asset availability, dependability, and performance by providing early warnings and spotting equipment problems before they become operational.
Common Use Cases of IoT-based Predictive Maintenance
To understand it better, let’s check out some common use cases of IoT-based predictive maintenance.
Discrete Manufacturing: To monitor the health of instruments like the spindle of milling machines prone to breaking, several discrete manufacturing sectors utilize predictive maintenance based on IoT.
Process Manufacturing: The best example is the steel industry, which employs IoT-based predictive maintenance to spot cooling panel leaks early on.
Gas and Oil: The oil and gas sectors can spot corrosion and pipeline degradation in dangerous conditions that are off-limits to humans thanks to the Internet of Things in maintenance.
Electric Power Industries: An IoT-based predictive maintenance system can guarantee a steady flow of electricity and spot early flaws in a turbine’s spinning components.
Railways: To find flaws in rails, wheels, bearings, etc., railways use force detectors, vision cameras, infrared, and sound sensors.
Construction: Predictive maintenance programs are used by the construction sector to keep track of the condition of large pieces of equipment, including bulldozers, loaders, lifts, and excavators.
Also Read: IoT in Manufacturing – Use-Cases, Benefits and Challenges
Businesses that Implemented IoT-based Predictive Maintenance
Several examples show how IoT-based predictive maintenance and quality control can be beneficial. Let’s check out them below:
1. Sandvik
Many industrial initiatives for creating future smarter factories have already heavily utilized the technology. For example, the Swedish engineering firm Sandvik collaborated with Microsoft to develop sensorized cutting tools.
The system combines data collecting, streaming analytics, and machine learning algorithms to alert engineers to tool bit maintenance needs or impending failures.
2. ABB
ABB, a multinational engineering company specializing in robotics, has created a predictive maintenance system for powering applications in manufacturing.
In this case, sensors, cloud computing, and machine learning algorithms work together to provide an overview of equipment performance to maintain the production schedule. It has benefited Tenaris, an Italian maker of steel pipes.
It monitors high- and low-voltage motors driving essential fans and pumps around the clock using predictive maintenance. The technology has been used to record and examine vibrations to highlight malfunctions and any variations in voltage or power that might point to a short circuit.
3. Coca Cola
Coca-Cola is another excellent example of a business that has employed IoT for quality control. The company installed sensors on its production line to continuously monitor the quality of its goods. The sensors gathered information on pressure, temperature, and other variables, then processed by machine learning algorithms.
As a result, the risk of making defective goods was decreased, and the consumer experience was enhanced. Coca-Cola could identify real-time abnormalities and resolve them before they become major issues.
4. General Electric
General Electric (GE) is a fantastic example of a business that has used IoT for predictive maintenance. GE installed sensors on its wind turbines that continuously gathered information about how well they operated.
Machine learning algorithms were utilized to analyze this data in real-time to predict when a wind turbine would most likely break. GE could execute repairs before the wind turbine malfunctioning to decrease downtime and boost productivity.
Future of IoT-enabled Predictive Maintenance
The Predictive Maintenance Market Report 2021-2026 estimates that the predictive maintenance market will be worth 28.2 billion by 2026.
IoT-enabled predictive maintenance holds a bright future for all kinds of enterprises. The following significant factors will influence how this technology develops:
Advanced Analytics and Machine Learning: These two areas will become increasingly crucial for predictive maintenance using IoT as technology advances. Organizations can make sense of the massive amount of data that IoT devices have collected using these approaches.
Companies can use predictive models and algorithms to identify subtle trends, correlations, and abnormalities in real-time data. It enables them to predict equipment breakdowns and maintenance requirements more precisely.
Edge Computing and Real-time Decision-making: The capacity to process data locally on IoT devices or nearby gateways is more common as edge computing expands.
By lowering latency, this trend makes quicker response times and real-time decision-making possible. Edge computing enables companies to immediately identify maintenance needs and abnormalities without relying on cloud-based analytics, enabling them to take prompt action to stop failures and improve performance.
Integration with Artificial Intelligence and Digital Twins: Combining IoT-enabled predictive maintenance with AI and digital twin technologies will open up new avenues for application. Using past data, AI systems can improve the accuracy of their predictions and advice over time.
To estimate maintenance requirements and improve asset performance, simulations and predictive modelling are made possible by digital twins, which are virtual versions of real assets.
Predictive Maintenance as a Service (PaaS): This model may become more prevalent in the future. Companies can use third-party suppliers who provide predictive maintenance solutions as a service rather than investing in creating their own IoT infrastructure and analytics skills.
PaaS lowers costs and implementation hurdles by enabling firms to access advanced analytics platforms, machine learning algorithms, and knowledge without requiring substantial in-house personnel.
Also Read: Top 12 Emerging IoT Technologies
How Can PixelCrayons Help You?
PixelCrayons can provide valuable assistance with industrial IoT predictive maintenance by offering their IoT development and implementation expertise. We help businesses design and develop IoT-enabled solutions incorporating sensors, connectivity, and data analytics capabilities.
Additionally, we serve as a reliable technology partner, guiding businesses through implementing IoT-based predictive maintenance solutions. Our expertise contributes to the successful integration of IoT devices, data analytics platforms, and machine learning algorithms, enabling businesses to leverage the power of predictive maintenance to drive improved performance and cost savings.
Over to You
IoT-based predictive maintenance is getting increasingly popular due to its advantages and utility. As a result, numerous sectors already use predictive maintenance to reduce costs and avoid accidents.
This technology anticipates failures using sensors, cloud storage systems, machine language algorithms, and analysis.
Consequently, putting IoT-based predictive maintenance into practice is a relatively simple process. All you have to do is contact IoT Application Development Services like PixelCrayons, start using the solutions, and increase your revenue!
FAQs
1. What is IoT predictive maintenance?
IoT predictive maintenance is a proactive strategy that employs IoT technology to anticipate and stop equipment breakdowns and maintenance problems before they happen. It entails gathering information from sensors installed in machinery, tools, or infrastructure, then analyzing it immediately or using historical trends.
Organizations can find trends, anomalies, and potential failure signs by using advanced analytics, machine learning, and predictive modelling approaches to the data. It lets them plan maintenance tasks at the ideal moment, minimizing costs, increasing operational effectiveness, and reducing downtime.
2. What is the application of IIoT in predictive maintenance?
The implementation of IoT technology, primarily in industrial settings, is known as the Industrial Internet of Things (IIoT). IIoT plays a significant part in predictive maintenance by providing real-time monitoring, collecting data, and analyzing industrial assets and equipment.
Continuous data streams are produced by IIoT equipment like sensors and connected machines, which collect data on various characteristics, including temperature, vibration, pressure, and energy usage. Advanced analytics and machine learning techniques are then used to process and analyze this data to find patterns, anomalies, and upcoming maintenance requirements.
IIoT predictive maintenance boosts operational efficiency across all industrial sectors by increasing equipment reliability, optimizing maintenance schedules, and reducing downtime.
3. What is preventive maintenance of IoT sensors?
The proactive servicing and maintenance of IoT sensors are called “preventive maintenance” to guarantee their continuing accuracy and usefulness. IoT sensors collect data, monitor a range of circumstances, and send it to centralized systems for analysis.
To maintain the sensors’ best performance, preventive maintenance includes routinely checking, calibrating, and cleaning them. Ensuring accurate measurements can also entail changing the batteries, upgrading the firmware, or making modifications.
Organizations can guarantee the dependability and quality of the data collected by IoT sensors, which is necessary for efficient predictive maintenance plans.
4. How is IoT used in predictive maintenance?
IoT is used in predictive maintenance by supplying real-time data from linked devices to enable ongoing equipment performance monitoring. Machines and infrastructure are equipped with IoT devices like sensors, actuators, and controllers to collect data on variables like temperature, vibration, pressure, and energy usage.
Advanced analytics methods are used to analyze this data after it has been delivered to centralized systems or cloud platforms. Using machine learning techniques, organizations can find data trends, abnormalities, and probable errors. Businesses can forecast maintenance requirements, plan preventative maintenance, and maximize the performance of their assets thanks to the insights gathered through this study.
5. How is IIoT used for the predictive maintenance of machines in the industry?
In the industry, IIoT is widely employed for proactive equipment maintenance. Industrial machinery incorporates IoT devices and sensors to collect real-time data on temperature, pressure, speed, and performance indicators. Advanced analytics and machine learning algorithms process and analyze this data once sent to centralized systems or cloud platforms.
Organizations can spot variations from typical operating circumstances and anticipate equipment problems by applying predictive models to the data. By proactively planning maintenance tasks, firms minimize downtime, maximize resource allocation, and increase machine dependability and productivity.
6. What is predictive maintenance for industrial IoT?
Utilizing IoT technology like sensors, connectivity, and data analytics to forecast and avert equipment breakdowns and maintenance difficulties in industrial settings is known as predictive maintenance for industrial IoT.
It involves monitoring and analyzing machine data to find trends, anomalies, and warning signs of future problems. Predictive maintenance for industrial IoT enables businesses to optimize maintenance schedules, cut downtime, and boost industrial machinery and equipment’s overall effectiveness and dependability by utilizing real-time and historical data, advanced analytics, and machine learning algorithms.
