Resources

Predictive maintenance (PdM) measures equipment condition to find early signs of failure and schedule repairs before breakdowns. This guide compares reactive, preventive, condition-based and predictive maintenance, and covers sensor choice and a five-step rollout.

AE sensors classified by frequency response and form: a comparison of the structure, pros and cons, and suitable applications of resonant, broadband and flexible sensors.

What an AE waveform tells you, how FFT analysis works, the key settings, and how spectra are used to distinguish signal sources and diagnose equipment.

How bearing and gear defects produce periodic AE signals and fault frequencies, how envelope analysis identifies the faulty component, and why AE is effective for slow-speed machinery and lubrication management.

How AE signals arise when cracks grow in metal equipment, how signal trends reveal damage progression, and how AE detects corrosion, wear and tearing compared with conventional inspection.

Definitions and meaning of the parameters used in AE signal analysis — hit, threshold, peak amplitude, rise time, duration, counts, energy and RMS — with illustrations.

The types of field noise picked up by AE sensors, and how to separate real AE signals through installation, frequency filtering, thresholds, feature analysis and learning the normal state.

A brief summary of Rina Solution’s AE monitoring cases: chemical plant pipe leaks, wear in high-temperature piping, recurring failures in rotating machinery, micro-defects in battery processes and machine tool tip wear.

How pipeline leaks generate AE signals, how AE differs from conventional leak detection, how two sensors locate a leak, and what factors affect sensitivity.

A checklist of what to verify when choosing an AE sensor: frequency band, mounting on curved surfaces, temperature and explosion-proof requirements, sensor spacing and data processing.