Conditioning monitoring is an evolution of predictive maintenance or Proactive maintenance. The origin is difficult to define, but predictive maintenance has made enormous progress over the last few decades. Nowadays, it has been addressed as one of the most innovative solutions for anticipating failures in machinery and is being used by a wide variety of industrial sectors.

Prediction maintenance could be applied to a large industrial sector when the cost of vibration sensors was competitive. This advantage reduced the cost of failures in comparison with the investment in the measurement equipment and analysis system. In the beginning, the systems were rudimentary and required specialized personnel to collect data and make the analysis.

The convergence of high-precision accelerometers and the ability to process the Fourier transform with the FFT algorithm allowed the development of rapid tools that can make a diagnosis on the actual condition of machines. Previously, vibration sensors were applied in just a few types of equipment due to their cost and the need for specialized personnel. These first concepts were complemented with other emerging technologies such as ultrasonic ,thermography, acoustic sensors, and directional microphones.

The first application of predictive maintenance was made by the Royal Air Force in the United Kingdom. It was found that the rate of failure increased after the repair or inspection of machines, even following the maintenance plans. This phenomenon was named the “Waddington Effect,” which led to condition monitoring. It was decided to adjust the maintenance programs and align them to the physical condition and frequency of use to reduce the Waddington effect. The process required the, analysis of much data, but the launch of this program reduced the number of failures.

Conditioning monitoring systems evaluate the vibration data and
determine the condition of the machine based on the analysis of amplitude and frequency. The original signal has raw data that have to be treated to produce a reference baseline. Sampling the evolution of the data during operation indicates the condition of the machine and, in the case of a failure, the data will present significant changes. Conditioning monitoring systems
have increased the reliability of machinery because they include new sensors while also using fast processing hardware and better algorithms for the signal process.

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