Vibration signal processing (Analog to Digital signal conversion ):

The vibration of a machine is a physical motion. Vibration transducers convert this motion into an electrical signal. The electrical signal is then passed on to data collectors or analyzers. The analyzers then process this signal to give the FFTs and other parameters. We will take a brief look at the processing of the signals, which finally provide us with the necessary information for condition monitoring. To achieve the final relevant output, the signal is processed with the following steps:

• Analog signal input
• Anti-alias filter
• A/D converter
• Overlap
• Windows
• FFT
• Averaging
• Display/storage.

Before we can discuss the above-mentioned digital signal processing steps, we need to take note of a few more terms and concepts.
A vibration or a system response can be represented by displacement, velocity and acceleration amplitudes in both time and frequency domains . Time domain consists of amplitude that varies with time. This is commonly referred to as filter-out or overall reading .

Analog to digital converters
The vibration waves collected by transducers are analog signals. Analog signals must be converted to digital values for further processing. This conversion from an analog signal to a digital signal is done by an Analog to Digital (A/D) converter. The A/D conversion is essentially done by microprocessors. Like any digital processor, A/D conversion works in the powers of two (called binary numbers). A 12-bit A/D converter provides 4096 intervals whereas a 16-bit A/D converter would provide 65 536 discrete intervals .

The greater the number of intervals, the better is the amplitude resolution of the signal. A 12-bit A/D converter would result in a resolution of 0.025% of the full scale, whereas a 16-bit A/D converter would yield a resolution of 0.0015%. It is thus possible to collect a signal with large and small amplitudes accurately.

It can be seen here that the sampling rate determines the highest frequency in the signal that can be encoded. The sampled waveform cannot know anything about what happens in the signal between the sampled times. Claude Shannon, the developer of the branch of mathematics called information theory, determined that to encode all the information in a signal being sampled, the sampling frequency must be at least double the highest frequency present in the signal. This fact is sometimes called the Nyquist criterion.