All operating machinery produces unique sounds that follow specific patterns. Power equipment is no exception—each has a stable acoustic signature during normal operation. When operational conditions deviate, the rhythm and tone of these soundprints immediately shift from their norm. The HIZ-GE-HSW Power Equipment Abnormal Sound Monitoring System by Hizhuo Technology leverages this fundamental principle to enable intelligent monitoring of equipment health.
Let’s take a look at the key features of the HIZ-GE-HSW system.
1. Real-time Online Monitoring with Tiered Fault Alerts
Equipped with bone-conduction acoustic sensors, the system continuously collects soundprint data from various power equipment in thermal power plants. It autonomously adapts to equipment operating conditions, dynamically building models and iteratively refining algorithms to precisely match real-time performance. Highly sensitive to early-stage micro-faults, it detects subtle anomalies before they escalate. When soundprints deviate beyond predefined thresholds, the system triggers a tiered alert mechanism, instantly notifying maintenance personnel and enabling proactive fault response.
2. Predictive Trend Analysis for Optimized Maintenance Decisions
By leveraging deep analysis of vast historical soundprint data, the system dynamically assesses equipment trends and estimates remaining service life, providing quantitative insights to support maintenance planning. Unlike traditional reactive repair models, this system enables predictive, on-demand maintenance, significantly reducing unplanned downtime, preventing major safety incidents and cascading equipment damage, enhancing overall power supply reliability, and effectively lowering comprehensive maintenance costs including materials and labor.
3. Multi-dimensional Data Processing for Accurate Condition Assessment
The system standardizes raw acoustic signals through preprocessing steps such as signal weighting, framing, and windowing. It then extracts multi-dimensional acoustic features—including time-frequency domain characteristics, amplitude, cepstrum, waveform, zero-crossing rate, wavelet coefficients, and kurtosis. Through integrated multidimensional analysis, it accurately determines equipment health status and identifies fault types, ensuring objective and reliable condition evaluations.
