Predictive maintenance in various industries has grown significantly due to advancements in industrial IoT analytics. The rise of sensor-enabled devices and powerful IoT platforms has enabled easy real-time data collection and equipment parameter logging, introducing a new era of IoT-based predictive maintenance.
Understanding IoT-Based Predictive Maintenance
How Does IoT-based Predictive Maintenance Work?
IoT predictive maintenance starts by gathering real-time data using sensor-equipped Internet of Things (IoT) devices attached to the equipment targeted for maintenance. This is IoT remote monitoring. The data then undergoes basic statistical analysis to understand its distribution, range of values, presence of missing values, and other statistical characteristics.
Following data collection by the remote monitoring system, it’s corrected and prepared based on its quality and type. Additionally, if the sensors monitor numerous parameters, we use feature engineering techniques and models to group them into relevant features that are important for analysis. This simplifies the process of analyzing the data.
Leveraging Machine Learning in Predictive Maintenance
The next steps in predictive maintenance involve machine learning. The choice of machine learning algorithms depends on the nature and volume of the parameters being monitored. The data type and specific business problems being addressed determine the selection of machine learning techniques. By fragmenting the data into test, validation, and prediction sets, machine learning (ML) models can be applied iteratively to refine their performance.
We assess the outcome of the ML model based on predefined fitness and performance criteria. Once a satisfactory model is developed, it’s implemented into the business processes to optimize maintenance operations. However, the process doesn’t end there. Continuous monitoring and improvement of the ML models are necessary to account for changes in environmental parameters and ensure their ongoing effectiveness.
We have a wealth of extensive experience at Bridgera in using machine learning and deep learning algorithms for industrial predictive analytics and IoT predictive maintenance. These algorithms have been employed individually, in combinations, or as ensembles of models, depending on the specific business problem, domain, data type, and availability.
Enhancing Failure Prediction with Recurrent Neural Networks (RNNs)
Recurrent neural networks (RNNs) play a vital role in machine failure prediction. RNNs are neural networks with loops that allow information to persist, making them well-suited for sequence and time series data. A recurrent neural network can be visualized as multiple copies of the same network, with each copy passing a message to the next one.
Long Short-Term Memory (LSTM) models, a form of RNNs, are particularly useful in the context of IoT analytics. LSTM models incorporate recurrent units that aim to remember past occurrences encountered by the network while disregarding irrelevant data. These LSTM models have been effectively utilized at the Bridgera ML factory for predicting breakdowns of equipment based on key parameters of importance over time.
A wide range of equipment has been subject to these LSTM models, enabling the prediction of various parameters that serve as key indicators of potential equipment issues. Parameters such as vibration, rotation, pressure, and voltage have been considered to forecast equipment breakdowns accurately.
To provide you with a glimpse of the outcomes, we have attached a sample output from our vibration parameter prediction model for a specific piece of equipment. This model has been tested across multiple equipment and parameter combinations, spanning different time windows.
Predictive maintenance powered by LSTM deep learning models has revolutionized the industrial landscape. It allows businesses to identify and address equipment issues proactively, minimizing downtime, reducing costs, and optimizing IoT equipment maintenance operations. As technology continues to advance, the potential for even more accurate and efficient predictive maintenance grows, promising a future of enhanced productivity and reliability in the industrial sector.
Steps to Integrate Predictive Maintenance into Your Operations
Transitioning from reactive to predictive maintenance can significantly enhance your operational efficiency, reduce equipment downtime, and extend asset lifespans. Here’s a step-by-step guide to help you integrate predictive maintenance into your organization:
1. Assess Current Maintenance Practices
Begin by evaluating your existing maintenance strategy. Identify failure trends, recurring issues, and the cost implications of unplanned downtime. This baseline will help quantify the potential ROI of a predictive maintenance system.
2. Define Clear Objectives
Set measurable goals such as reducing maintenance costs, improving asset uptime, or extending equipment life. Clear objectives will guide the technology selection and implementation roadmap.
3. Choose the Right Technology Stack
Select IoT sensors and data collection tools that align with your equipment and operational environment. Make sure they can capture key data points such as vibration, temperature, pressure, and machine run-time.
4. Implement Data Infrastructure
Deploy a platform to collect, store, and process sensor data. Cloud-based solutions offer scalability and real-time analytics capabilities, enabling faster insights and easier integration across sites.
5. Leverage Advanced Analytics and Machine Learning
Utilize AI/ML models to analyze historical and real-time data, identify patterns, and predict failures before they occur. Tools like LSTM (Long Short-Term Memory) neural networks can significantly enhance prediction accuracy.
6. Partner with a Trusted IoT Provider
Working with a partner experienced in predictive maintenance solutions can simplify the deployment and provide ongoing support. Bridgera’s Predictive Maintenance Solution offers a comprehensive platform to help businesses move from reactive to proactive maintenance strategies.
7. Train Your Team
Ensure your maintenance and operations teams understand the new tools and workflows. Conduct training sessions to familiarize them with dashboards, alerts, and actionable insights.
8. Monitor and Optimize
Continuously monitor system performance and maintenance outcomes. Use insights to refine models, improve predictions, and optimize asset management strategies over time.
Bridgera: Your Perfect Choice in Cutting-Edge IoT Predictive Maintenance Solutions
We are leaders in IoT solutions & services. We leverage our expertise in machine learning and data science to design and implement customized predictive maintenance solutions that empower businesses to optimize their IoT equipment maintenance operations and achieve significant cost savings.
Contact Bridgera today to discuss your specific needs and explore how our customized predictive maintenance solutions can streamline your industrial maintenance practices.
About Bridgera: Bridgera effortlessly combines innovation and expertise to deliver cutting-edge solutions using connected intelligence. We engineer experiences that go beyond expectations, equipping our clients with the tools they need to excel in an increasingly interconnected world. Since our establishment in 2015, Bridgera, headquartered in Raleigh, NC, has specialized in crafting and managing tailored SaaS solutions for web, mobile, and IoT applications across North America.
About the Author: Gayatri Sriaadhibhatla is a seasoned writer with a diverse portfolio spanning multiple industries. Her passion for technology and a keen interest in emerging IoT trends drive her writing pursuits. Always eager to expand her knowledge, she is dedicated to delivering insightful content that informs the audience.