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.
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.
The Next Steps in Predictive Maintenance Involve Machine Learning
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.
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.
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 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.