Case Study: Resolving a Complex Vibration Issue Using Data from PR6423/110-100 and PR6424/006-030

What Was the Elusive Vibration Plaguing a Critical Compressor?

Within a major industrial facility, a critical process compressor began exhibiting a persistent, high-frequency vibration that defied all conventional troubleshooting methods. This was no ordinary hum or shake; it manifested as a sharp, metallic ringing during specific operational phases, particularly when the compressor transitioned through certain speed thresholds. The vibration was severe enough to trigger safety shutdowns on multiple occasions, leading to significant production losses and raising serious concerns about a potential catastrophic failure. Initial investigations using standard vibration analysis equipment failed to identify the root cause. The maintenance team had meticulously checked all the usual suspects: imbalance, loose components, and resonance issues. Yet, the problem persisted, hidden within the complex vibration signature of the rotating machinery. The elusive nature of this vibration made it clear that a more sophisticated approach to data collection and analysis was required—one capable of capturing the full spectrum of the machine's dynamic behavior under actual operating conditions. This challenge underscored the need for a robust control and power system to manage such critical assets, often supported by reliable components like the 1769-PA2 power supply module, which ensures stable operation for monitoring and control hardware.

How Was a Comprehensive Sensor Network Deployed?

To tackle this challenging diagnostic puzzle, we designed and deployed a comprehensive sensor network that leveraged the specific strengths of different vibration monitoring technologies. The backbone of this system was the PR6423/110-100 eddy current proximity probe system, installed at key radial positions on the compressor shaft. These sensors are exceptional for measuring the relative vibration between the shaft and bearing housing, providing precise, real-time data on shaft displacement—a critical parameter for identifying issues like misalignment and certain bearing problems. Complementing these, we strategically deployed PR6424/006-030 accelerometers directly onto the bearing housings. These piezoelectric sensors excel at capturing high-frequency vibration signals and acoustic emissions associated with bearing defects, gear mesh problems, and other anomalies that shaft proximity probes might miss. While the PR6424/000-100 configuration was considered for its broader frequency response, the PR6424/006-030 was specifically selected for its optimal sensitivity in the precise frequency range where the suspected issue was manifesting. This dual-sensor strategy created a complete diagnostic picture, capturing both shaft relative motion (via PR6423/110-100) and absolute casing vibration (via PR6424/006-030). This ensured no relevant data across different frequency ranges and vibration types was overlooked. The data acquisition for this network was managed by a dedicated control system, whose reliability often depends on precise output modules like the 1769-OW8 for triggering alarms or auxiliary equipment based on sensor readings.

What Did the Correlated Data Analysis Reveal?

The data analysis phase commenced with the collection of simultaneous, time-synchronized measurements from both the PR6423/110-100 shaft displacement probes and the PR6424/006-030 accelerometers. Data was captured across various operational states, including the critical startup ramp, steady-state operation at different loads, and the shutdown sequence. Initially, the overall vibration levels in the data from the PR6423/110-100 system appeared deceptively within acceptable limits. This system primarily focuses on lower frequency components related to imbalance and misalignment. However, when we isolated and examined the high-frequency data stream from the PR6424/006-030 accelerometers, a different and more alarming story emerged. Applying advanced signal processing techniques—such as high-resolution spectral analysis and envelope detection (demodulation)—revealed subtle but consistent bearing fault frequencies. These tell-tale signatures were otherwise completely buried in the overall noise floor. The true diagnostic breakthrough occurred when we performed cross-channel phase analysis. By comparing the phase relationship between the high-frequency events from the accelerometers and the precise angular position of the shaft from the proximity probes, we discovered a direct correlation. Certain vibration spikes detected by the PR6424/006-030 were occurring at specific, repeatable angular positions of the shaft, indicating a mechanical relationship between shaft orientation and the generation of high-frequency stress waves.

Was the Root Cause a Single Issue or a Complex Interaction?

After thorough analysis of the correlated data sets, the root cause was definitively identified not as a single fault, but as a combination of two interrelated issues creating a synergistic failure mode. First, the data from the PR6424/006-030 accelerometers provided unambiguous evidence of early-stage spalling on the outer race of one of the compressor's main bearings. This microscopic defect was generating high-frequency stress waves each time a rolling element passed over the damaged area. These signals were essentially invisible to the displacement-based PR6423/110-100 system operating alone. Second, a subtle shaft misalignment condition was also present. This misalignment was barely detectable in the overall 1X vibration amplitude from the PR6423/110-100 probes but was clear in its phase readings. This misalignment was exacerbating the bearing issue by creating uneven and dynamically changing loading across the bearing surfaces. In isolation, the slight misalignment might have been tolerable for some time, and the early-stage bearing spalling might have progressed very slowly under ideal, evenly distributed loads. However, their combination created a perfect storm: the misalignment concentrated cyclical forces onto a specific zone of the bearing race, dramatically accelerating the deterioration process precisely where the initial spalling had begun. This complex interaction explained why the problem had been so stubbornly difficult to diagnose with conventional, single-sensor approaches and why it manifested most severely during specific operational transitions when internal load vectors shifted.

How Was the Solution Executed and Verified?

With the root cause precisely pinpointed, the corrective action plan was clear and targeted. The solution involved a two-pronged approach: replacing the damaged bearing with a new, high-precision unit and performing a meticulous laser alignment of the compressor rotor relative to its driver. During the repair shutdown, our analytical findings were visually confirmed; the removed bearing showed clear signs of early spalling exactly in the circumferential zone predicted by our analysis of the PR6424/006-030 accelerometer data. Following the mechanical repairs, we initiated a comprehensive verification process using the same installed sensor network. The PR6423/110-100 proximity probe system played a crucial role in this phase, providing clear, quantitative evidence that shaft alignment had been restored to within tight, specified tolerances. Most importantly, the high-frequency spectral components and envelope signatures that had been so prominent in the PR6424/006-030 data had completely disappeared, indicating the source of the stress waves was eliminated. The compressor was monitored through multiple start-stop cycles and under various load conditions. Both sensor types consistently confirmed the total elimination of the problematic high-frequency vibration. The compressor returned to smooth, stable, and quiet operation without any further safety shutdowns. Follow-up vibration measurements taken several weeks later continued to show optimal levels, confirming the permanence and effectiveness of the repair. Ensuring such precision repairs and subsequent stable operation often relies on a fully integrated control system, where a consistent and clean power supply, such as that provided by a 1769-PB4 module, is fundamental for all monitoring and control electronics.

What Were the Key Lessons in Sensor Selection and Application?

This challenging case yielded several invaluable lessons for vibration analysis and predictive maintenance strategy. It clearly demonstrated the complementary nature of different sensor technologies. The PR6423/110-100 proximity probe system proved itself indispensable for monitoring shaft-centric phenomena like misalignment, imbalance, and shaft orbital motion, yet it inherently possessed limitations in detecting very high-frequency bearing defects. Conversely, the PR6424/006-030 accelerometers excelled in capturing the high-frequency stress waves and acoustic emissions generated by early-stage bearing surface degradation—precisely the signals that the proximity probes could not see. The pivotal insight was that neither sensor technology alone would have solved this complex problem; it was the strategic combination of their complementary capabilities that enabled the successful diagnosis. While the PR6424/000-100 variant offered a different frequency response, the specifically tuned sensitivity of the PR6424/006-030 model proved ideal for capturing the specific fault signatures in this application. This experience strongly reinforces the principle that complex machinery vibration issues frequently demand a multi-faceted, multi-sensor approach to data collection. Utilizing sensors with different operating principles (displacement vs. acceleration) and frequency responses is essential to construct a complete and accurate picture of machine health. The investment in a comprehensive sensor network and the analytical effort to correlate the data paid significant dividends. It prevented what could have escalated into a catastrophic bearing failure, avoiding associated safety risks, extensive secondary damage, and prolonged, costly downtime for the entire production line.

Vibration Analysis Compressor Monitoring Sensor Data Analysis

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