Predictive Maintenance in Deep Learning
Deep learning models have already proven to be highly effective in the domain of economics and financial modeling, dealing with time-series data. Similarly, in predictive maintenance, the data is collected over time to monitor the health of an asset with the goal of finding patterns to predict failures. Consequently, deep learning can be of significant aid for predictive maintenance of complex machinery and connected systems.
Determining when to conduct maintenance on equipment is an exceptionally difficult task with high financial and managerial stakes. Each time a machine is taken offline for maintenance, the result is reduced production or even factory downtime. Frequent fixes translate into clear losses, but infrequent maintenance can lead to even more costly breakdowns and catastrophic industrial accidents.
This is why the automated feature engineering of neural networks is of critical importance. Traditional ML algorithms for predictive maintenance depend on narrow, domain-specific expertise to hand-craft features to detect machine health issues. Whereas a neural net can infer those features automatically with sufficiently high-quality training data. It is, therefore, cross-domain and scalable.
In particular, recurrent neural networks (RNN) with Long-short-term-memory (LSTM) cells or gated-recurrent-units (GRU) can predict short-range to mid-range temporal behavior based on past training time in the form of time series.
Fortunately, there is a deluge of research activities on RNN with the goal of applying them to the field of natural language processing and text analytics. All the knowledge in this area of research can be leveraged to apply in the setting of an industrial application. For example, compute-optimized RNNs can be used for manufacturing jobs where the computational load is minimized without sacrificing the predictive power too much. It may not be best performing for an NLP task, but can be sufficiently powerful for predicting potential issues with machine health parameters.
Of course, a human expert will review the predictions of a deep learning system to finally decide about the maintenance work. But in a smart, connected factory, using such prediction machines along with engineers and technicians, can save a manufacturing organization money and manpower ultimately improving downtime and machine utilization.
In fact, the adoption of machine learning and analytics in manufacturing will only improve predictive maintenance. Predictive maintenance is expected to increase by 38% in the next five years according to PwC. This article from Microsoft provides more information on the topic:
Deep learning for predictive maintenance with Long Short Term Memory Networks
Factory Input Optimization
A manufacturing organization’s profitability critically depends on optimizing the physical resources going into the production process as well as supporting those processes. For example, electrical power and water supply are two crucial factory inputs that can benefit from optimization.
Complex optimization processes and strategies are often employed for maximizing the utilization of these essential resources. As the factory size and the machine-to-machine interaction grows, the flow of these resources become intractably complex to manage with simple predictive algorithms. This is when powerful learning machines like neural nets need to be brought into the game.
Deep learning systems can track the pattern of electricity usage as a function of hundreds of plant process parameters and product design variables and can dynamically recommend best practices for optimum utilization. If the organization is moving toward renewable energy adoption, predictions from deep learning algorithms can be used to chart out the optimum transition trajectory from fossil-fuel dependency to a sustainable energy footprint. This kind of paradigm change is difficult to handle using classical predictive analytics.
Information-system-enabled smart manufacturing has increased productivity and quality of industrial organizations, big and small, for quite a few decades now. In this smart manufacturing setting, usage of data analytics, statistical modeling, and predictive algorithms have increased by leaps and bounds, as the quality and propensity of machine-generated and human-generated data improved over time.
The industrial revolution, which started with Henry Ford’s assembly line at the turn of the past century, was aided throughout the 20th century by innovations in automation, control systems, electronics, sensors, digital computing, and the internet. Big data revolution of the 21st century is poised to finally take it to a whole new level by unleashing exponential growth opportunities.
To take full advantage of this data explosion, deep learning, and associated AI-assisted techniques, must be integrated into the toolkit of modern manufacturing systems, as they are exponentially more powerful than classical statistical learning and prediction systems.
Deep learning is able to integrate seamlessly with the ambitious goals of Industry 4.0 — Extreme automation, and Digital Factory. Industry 4.0 is designed around the constant connection to information —sensors, drives, valves, all working together with a single common goal: minimizing downtime and increasing efficiency. Algorithmic frameworks like a deep neural network, which is flexible enough to work with a variety of data types as they stream in continuously, are the right choice for handling that particular type of task.
The resulting increase in productivity and quality is expected to go far beyond the narrow goal of satisfying corporate profitability. Tomorrow’s smart manufacturing will enrich the lives of billions of consumers by providing goods and services with high quality and at an affordable cost. Society, as a whole, should benefit from such a paradigm-shifting transformation.
The future is bright, and we look forward to it!
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