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Using AI for predictive maintenance in
A key part of smart manufacturing and a modern factory approach involves real-time monitoring of machinery operating conditions and assembly line processes. AI models can automate monitoring tasks, resulting in reduced downtime and improved production quality.
On an assembly line, time is critical. Machine failure and defects result in downed lines, inefficient processes, and lost business. In fact, equipment maintenance expenses alone make up between 15-40% of a manufacturer’s total production costs. Current maintenance approaches often feature blunt techniques such as blind checks or are centered around machine usage alone. Without specific and reliable data on machinery operating conditions, manufacturers tend to be cautious and undergo more maintenance than is necessary, leading to excessive downtime and lost production. Even though most manufacturers continue to favor preventative maintenance approaches, AI-enabled predictive maintenance is significantly more cost effective and efficient, resulting in savings of 30-40% and a tenfold return on investment. These benefits are substantial, especially given that equipment which fails before its expected lifetime can lead to more severe consequences, including significant downtime and costs that are over 10 times as much as a maintenance program.
The opportunity for deep learning
Every piece of equipment - whether an assembly line manufacturing machine or even a medical device - has an estimated lifespan that depends on numerous factors like usage and material. Continuous equipment monitoring results in a high volume of data that can be laborious and costly for a human operator to analyze. In contrast, deep learning models can handle large volumes of data efficiently, and they can accurately identify defects and anomalies with much greater speed and consistency, saving time and money.
How does the model work ?
Deep learning models are also flexible, and this particular application can be built using image, sound, or tabular data. From that data, the AI model will first learn the complex relationship between the input and whether or not it represents a malfunction. For example, the model could learn to distinguish between images of malfunctioning or defective equipment and functioning equipment. Similarly, it could learn to identify the sounds of a malfunctioning machine or spot failing equipment based on sensor data. In fact, different data types can even be combined to form a multi-modal approach!
What can be archieved
Using deep learning to quickly trage malfunctioning or defective machinery can minimise production delays and errors.
Regardless of the type of data being used, this model needs a dataset of examples to learn from. These examples should be representative of the data being used in production, and they should be categorized as either functioning or malfunctioning. For our specific example, we’ll be using audio recordings gathered from microphones mounted on key parts of the machine. To prepare the data, we converted the audio recordings to spectrograms, which are a visual representation of the intensity of different frequencies present in a signal. Click below to compare the audio recording of an industrial solenoid valve to its spectrogram.
Where to learn more?
If you would like to learn how the audio recordings are preprocessed to create spectrograms and then used in a binary image classification model, check out our tutorial.
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