Predictive maintenance has become increasingly prevalent in various industries, thanks to the advancements in industrial IoT analytics. With the rise of sensor-enabled devices and powerful IoT platforms, it has become possible to gather real-time data and log equipment parameters effortlessly. This has opened doors to a new era of Predictive Maintenance.  

How does IoT predictive maintenance work?

The process of IoT predictive maintenance begins with the collection of real-time data using sensor-enabled Internet Of Things devices attached to the equipment under consideration for maintenance. This is essentially IoT remote monitoring. The data is then subjected to basic statistical analysis to gain insights into its distribution, range of values, presence of missing values, and other statistical parameters. 

Once the remote monitoring system gathers the data, it undergoes correction and wrangling based on its health and type. Additionally, if the sensors are monitoring numerous parameters, we employ feature engineering techniques and models to group them into meaningful features of importance. This helps in simplifying the analysis process. 

The next steps in predictive maintenance involve machine learning

Next comes the application of various machine learning algorithms, considering the nature and volume of the parameters being monitored. The choice of machine learning techniques depends on the data type and the specific business problems being addressed. By fragmenting the data into test, validation, and prediction sets, ML models can be applied iteratively to refine their performance. 

Based on pre defined fitness and performance criteria, we assess the outcome of the ML model. Once a satisfactory model has been developed, it is implemented into the business processes to optimize maintenance operations. However, the journey doesn’t end there. Continuous monitoring and improvisation of the ML models are necessary to account for changes in environmental parameters and ensure their ongoing effectiveness. 

At Bridgera, we have extensively worked with various 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

Recurrent Neural Networks (RNNs) play a vital role in this 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 predictive analytics. LSTM models incorporate recurrent units that aim to remember past occurrences that the network has encountered 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 piece of specific 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 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.

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