What has the holographic principle in common with predictive maintenance?

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What has the holographic principle in common with predictive maintenance?

By Pavel Konečný, CEO & Co-Founder of Neuron soundware

Physics defines sound as a vibration that propagates as an audible wave of pressure through a transmission medium such as gas, liquid, or a solid. Most of the sounds we hear are generated from solid materials, including human speech. Although we need to breathe in order to speak, the muscle tension of the vocal cords must be precisely managed in order to generate the voice.

If humans happen to hear a strange noise, such as an iron spoon slowly moving on the glass or a low-frequency roar from a distance, this can cause the body to automatically respond with goosebumps as a reaction to potential danger. Richard Feynman, the renowned theoretical physicist, once shared a thought that we could calculate what is happening in water by observing waves in a corner of a swimming pool. This state, when what is happening inside an object might be entirely contained in surface fluctuations, is known as ‘holographic principle’. This principle is the basis of many predictive technologies that are used for the early detection of mechanical failures.

Machines are mostly made from metal, which is very good in sound distribution. Therefore, physics matters such as degradation inside a bearing, manifest in sounds. Degradation such as this can be measured noninvasively on the surface of the bearing. As a result, they are used to determine the mechanical state of the machine or even in some cases, calculate the remaining useful life. Such diagnostic methods have been applied in industry for decades now, in particular for machines with a simple type of movement such as rotary equipment, where simple equations work (primary scope of the vibro diagnostics technology).

Failure predictions in the world of complex machines

However, the world is full of complex machines (e.g. robots, cranes, printing machines, and engines). These machines are characterized by factors such as multiple moving axes, influencing joints, gearboxes, linear, diperiodic operation, and so on. This results in the question of whether or not you would be able to get all of these aspects under control.

To analyze the state of complex machines accurately, many sensors and very complicated equations would need to be applied. However, a process such as that seems too expensive and impractical; apart from a few exceptions such as a turbine of a nuclear power plant covered by hundreds of sensors. In the real world, we are limited by the number of sensors, the amount of available data, and the cost of computing power. The most common machines are usually monitored by just one, or maybe a few, sensors. Therefore, the signals coming from different sources get overlapped easily, and sometimes to the human ear, it can sound like a complete mess.

Here comes the beauty of modern algorithms of Artificial Intelligence (AI) and machine learning, such as deep neural networks that can process complex signals in order to determine the health status of a machine. A self-learning algorithm is capable of remembering how different issues manifest and yet calibrate to a specific operation of every machine individually. Complex algorithms of artificial intelligence allow monitoring each sensor individually or in combinations. Here we can see the future of the prediction of mechanical issues. With the quick progress in the Internet of Things (IoT) technologies, we can expect a higher amount of sensor data available for analyses, including large sound data sets that can be nowadays processed by AI at an affordable price.

Moreover, we can envision that with the increasing capability of 3D-printed components, sensors could be soon embedded within the material. With the combination of edge computing, lots of data could be processed locally creating a new sense – machines will be able to feel their pains and call for help if needed.