Case Study | Tracing the Root Cause of Vibration Anomalies: A Loose Bolt Identified with Precision Through Data Analytics

In metallurgical production processes, the ball mill is the core equipment of the grinding stage, and the operational condition of its drive system directly affects the stability of the entire production line. If bolts at connection points become loose, this can, at best, exacerbate equipment vibration; at worst, it may lead to damage to gears and bearings, or even trigger unplanned shutdowns, resulting in significant economic losses for the enterprise.

Today, through a real-world operations and maintenance case, we’ll show how an industrial intelligent predictive maintenance platform leverages real-time monitoring data to detect anomalies early, pinpoint faults with precision, and mitigate equipment safety risks at the lowest possible cost.

I. Data Alert: Equipment vibration remains consistently above the threshold, with risks quietly escalating.

In late May 2026, our monitoring revealed that the vibration velocity at the drive end of the pinion bearing housing of Ball Mill No. 2 at a certain metallurgical enterprise was steadily increasing, with peak values occasionally exceeding 18 mm/s—far surpassing the equipment’s safe operating limits.

The platform’s 24/7 online monitoring system promptly detected this abnormal change, triggering multiple tiers of alerts in succession and automatically pushing an alert summary. Details such as the alert time, anomaly type, threshold‑exceeding values, and device locations are immediately visible, buying valuable time for technical analysis.

II. Graph-Based Analysis: Systematic Troubleshooting to Identify the Root Cause of the Fault

Upon receiving the alarm, the technical team immediately retrieved the vibration trend curves and spectral data for in-depth analysis. The results indicated that the equipment’s vibration energy was primarily concentrated at 75 Hz and its harmonic frequencies, with no discernible sideband modulation—thereby ruling out gear wear as the cause. Considering the gradual increase in vibration levels and the absence of sudden spikes, the team concluded that the fault was not due to damage to a critical component but rather to loosened foundation bolts securing the bearing housing. They promptly issued maintenance recommendations to the field team.

Compared with the traditional manual inspection approach that relies on experiential judgment, this method leverages vibration spectra and trend data for comprehensive analysis, enabling precise fault diagnosis and traceability while effectively preventing unnecessary disassembly and misdiagnosis.

III. On-site Maintenance: Tighten Bolts to Quickly Eliminate Potential Hazards

According to the diagnostic plan, on-site personnel shut down the equipment and performed a disassembly inspection, which confirmed that the mounting bolts on the drive-end platform of the bearing housing were significantly loose. This seemingly minor issue caused periodic oscillations in the equipment’s shaft system, continuously amplifying the vibration amplitude.

This maintenance procedure was straightforward and highly efficient; no high‑value components such as gears or bearings needed to be replaced. By simply tightening loose bolts, the fault was resolved, significantly reducing both repair costs and downtime.

IV. Effect Verification: Data has returned to normal, and the equipment has stabilized.

Following the equipment’s restart, the monitoring platform updated operational data in real time: the vibration velocity at the drive end of the ball mill bearing housing decreased from an exceedance of 18 mm/s to below 4 mm/s, the vibration trend curve returned to a stable state, and the abnormal peaks in the spectrum also dropped significantly.

From anomaly alerting to fault remediation and effectiveness verification, the entire process is swiftly closed-looped, ensuring that this “invisible bomb” threatening the production line has been completely neutralized.

V. Case Summary: The Core Value of Intelligent Predictive Maintenance

The successful resolution of this ball mill failure vividly demonstrates the three key advantages of intelligent equipment maintenance over traditional approaches: ✅ Early trend detection: By continuously monitoring operational data, subtle anomalies are identified in advance, nipping major failures in the bud and ensuring the safe operation of critical equipment.

✅ Precise Fault Localization: Leveraging data-driven analysis to identify fault types, eliminating reliance on guesswork and experience‑based repairs, thereby significantly reducing spare‑part waste and labor costs.

✅ Efficient closed-loop process: Enables end-to-end collaboration across early warning, diagnosis, maintenance, and verification, minimizing downtime and ensuring continuous production line operation.

In the era of industrial digital transformation, equipment maintenance has long since moved beyond the reactive “fix‑it‑when‑it‑breaks” approach. Zhongyun Technology specializes in predictive maintenance for industrial equipment, using data to “take the pulse” and diagnose machinery, ensuring that even the smallest potential faults are detected and addressed—providing comprehensive support to ensure the stable, end‑to‑end operation of industrial assets throughout their entire lifecycle.

What other hidden faults have you encountered in equipment operations and maintenance? Feel free to share your experiences in the comments!

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