How reliable is vibration trending for predicting remaining life?

Vibration trending provides a reasonable estimate when the fault mechanism produces a consistent growth pattern. Bearing degradation typically follows an exponential curve. Accuracy improves with more data points, consistent measurement conditions, and understanding of the failure mode. Estimates should be treated as guidance, not precise predictions.

What is the difference between linear and exponential vibration growth?

Linear growth means vibration increases at a constant rate over time (e.g., +0.5 mm/s per month). Exponential growth means the rate of increase accelerates over time - common in bearing degradation where damage creates more damage. Exponential growth is more dangerous because the final failure can be sudden.

When should I take action based on vibration trends?

Action should be planned when vibration enters Zone C (per ISO 20816/10816) or reaches the alarm level. Schedule maintenance before the danger level is reached. As a rule of thumb, plan corrective action when vibration has increased by 50% above baseline, and take urgent action at 100% increase or when approaching alarm limits.

What affects the accuracy of vibration trend predictions?

Key factors: consistency of measurement point and direction, sensor mounting quality, operating conditions (load, speed, temperature), measurement interval regularity, and the assumption that the growth mechanism remains unchanged. External events (impacts, process changes) can alter the trend.

How often should vibration measurements be taken for trending?

Frequency depends on criticality: continuous monitoring for critical machines, monthly for important machines, quarterly for general. When a rising trend is detected, increase frequency: weekly or daily as alarm levels approach. The P-F interval (time from detectable fault to functional failure) determines the minimum monitoring frequency.

免費工程工具 #034

根據振動趨勢預估剩餘壽命

根據振動趨勢資料預估剩餘可用壽命 (RUL)。使用線性、指數或冪律成長模型預測達到警報與危險等級的時間。

RUL 估算器預測性維護趨勢分析

基線振動 V_基線 (mm/s RMS)

目前振動 V_目前 (mm/s RMS)

距基線時間 (天)

時間單位

警報等級 V_警報 (mm/s RMS)

危險等級 V_危險 (mm/s RMS)

成長模型

快速預設

結果

距警報剩餘時間

—

距危險剩餘時間

—

成長率

—

成長模型

—

振動增加量

—

預測振動等級

⚠️ 信心備註： 此估算假設目前的成長模式持續不變。實際剩餘壽命取決於故障機制、運轉條件、負載變化及維護措施。僅供規劃參考，非保證。更多資料點與一致的測量條件可提高準確度。

線性成長模型

假設振動以恆定速率增加：

線性模型適用於漸進式磨損過程，例如因侵蝕或堆積導致的不平衡增長。

指數成長模型

假設振動成長率與目前等級成正比（損傷加速）：

指數模型最能代表軸承退化與疲勞裂紋擴展，其中損傷會引發更多損傷。

冪律模型

可表示次線性與超線性成長的通用模型：

冪律適用於混合退化模式。指數 p 決定成長行為：p<1 為減速，p=1 為線性，p>1 為加速。

應選擇哪種模型？

模型	最適用於	行為
線性	漸進式磨損、侵蝕、堆積	恆定變化率
指數	軸承損傷、裂紋擴展	加速 — 最保守
冪律	混合/未知機制	靈活 — 適應資料形狀

實務範例

範例 — 泵浦軸承退化

已知： V_baseline = 2.5 mm/s, V_current = 4.2 mm/s, elapsed = 90 days, alarm = 7.1 mm/s

指數模型：

k = ln(4.2 / 2.5) / 90 = ln(1.68) / 90 = 0.5188 / 90 = 0.00577 /day

Time to alarm: t_alarm = ln(7.1 / 2.5) / 0.00577 = 1.0438 / 0.00577 = 181 days from baseline

Remaining = 181 – 90 = 91 days from now to alarm level

P-F 區間： 可偵測故障起始點 (P) 與功能失效點 (F) 之間的時間，決定了您能獲得多少預警時間。對於滾動軸承，P-F 間隔通常為 1–9 個月，具體取決於轉速、負載及潤滑條件。