QM30VT3 3-Axis Vibration Sensor Overview
An overview of the QM30VT3 3-Axis Vibration Sensor's functionality
高效能三軸振動監測可透過精準的即時診斷,協助提早發現故障、縮短停機時間並簡化預測性維護,讓團隊在問題惡化前採取行動。高解析度三軸感測能更精確地偵測不平衡、鬆動或軸心偏斜等問題,協助團隊避免突發故障及高額維修成本。提供外部供電及電池供電兩種方案,無論設備位於何處都能輕鬆收集並傳送振動資料。
3-Axis Vibration Sensors
在 X、Y、Z 三個軸向上進行超低雜訊振動監測,可更完整地掌握設備健康狀態,安裝方式也比雙軸感測器及多數三軸 MEMS 感測器更有彈性;比他牌三軸 MEMS 感測器的偵測雜訊最高可高出三倍。Banner 振動感測器在三個軸向上皆具備超低雜訊效能,能擷取顯示關鍵早期故障的振動模式,包括角度不對心及動態不平衡等其他感測器可能遺漏的問題。只要記錄感測器各軸與設備軸向的對應關係,即可依最適合應用需求的方向安裝;無論安裝方向或位置為何,都能偵測從細微不平衡到早期軸承磨損等各種問題。
Banner 無線電池供電三軸振動監測結合先進的振動監測技術,以及無線與電池供電連線的便利性和彈性,可從廠區各處取得重要的設備健康資訊。Q45VA3C 與 Q45VT3 均能提供可靠效能及順暢整合。電池壽命超過 24 個月,加上高效低功耗運作,可將維護需求降至最低;可調整的回報間隔還能進一步延長電池壽命。產品提供 900 MHz 或 2.4 GHz 規格(依機型而定),可符合不同地區及場域的無線射頻需求。兩者皆相容於所有 Banner MultiHop 閘道器,包括 DXM1200-X2R2,讓使用者可將無線及有線狀態監測感測器連接至同一台閘道器。
採線路供電的 QM30VT3 感測器內建 VIBE-IQ,利用機器學習建立振動基準值,並自動產生警告與警報門檻,因此無須閘道器或專業知識,任何人都能監控設備資產。
在故障惡化前,偵測馬達、齒輪箱及其他設備的早期故障徵兆。6 Hz 至 5.3 kHz 的頻率範圍可涵蓋低速與高速設備,偵測不平衡、不對心、軸承磨損及齒輪嚙合等問題。
採用 26.8 kHz 高速取樣率,可從低轉速設備擷取詳細的低頻振動資料及短暫衝擊事件(例如早期軸承故障),清楚解析高頻瞬態訊號。
可調式最高頻率(FMax)讓使用者能依設備速度與故障特性,調整頻率範圍及取樣長度。較高的 FMax 可擷取更廣的頻率範圍,取樣時間較短,預設解析度適合偵測高速設備的故障。較低的 FMax 則能逐步提高取樣解析度並延長取樣時間,適合偵測極低速設備的故障。
高頻包絡模式(HFE)會濾除低頻訊號並隔離高頻訊號,更容易偵測軸承磨損及潤滑問題等早期故障。搭配較低的 FMax 設定,可在隔離高頻訊號的同時延長取樣時間並提升解析度;這對偵測低速設備中容易被強烈低頻振動掩蓋的微弱高頻故障特徵至關重要。
123當應用需要持續取得資料,或多個感測器集中安裝在同一撬裝設備、機台或工作站時,線路供電感測器是理想選擇。在此範例中,DXMR90-X1E 控制器會收集多個 QM30VT3 振動感測器的資料,其中三個感測器直接連接控制器,以取得電力並傳輸資料;其餘感測器則連接 R70 無線通訊節點及電源供應器,再由 R70 將資料以無線方式傳送至連接控制器的另一個 R70。
1234電池供電感測器適合由第三方進行廠務監控整合,或感測器位於難以佈設電源的遠端位置。如圖所示,多個 QM30VT3 振動感測器採用電池供電:三個無線一體式裝置(QM45VA3C)將感測器、MultiHop 無線通訊模組及電池整合於單一外殼中,另外兩個感測器則分別連接獨立的 MultiHop 無線通訊模組(Q45VT3)。資料透過無線通訊傳送至 DXM1200-X2R2 控制器,線路供電的振動感測器也可直接連接至該控制器。
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An ultra-low noise third axis enables mounting in any axial orientation without sacrificing accuracy and reliability, which is critical when space or access limits how a sensor can be positioned. Most 3-axis MEMS sensors have up to three times more noise density in their third axis (typically Z), resulting in reduced reliability and less installation flexibility.
A larger frequency bandwidth improves performance by capturing fault signals that narrower-bandwidth sensors cannot detect—especially high-frequency indicators of early bearing and gear damage. For example, expanding from 4000 Hz to 5300 Hz allows detection of subtle, high-frequency impacts that may not appear below 4 kHz. This enables earlier intervention and makes the sensor effective across a broader range of machine speeds and fault types.
The built-in VIBE-IQ algorithm processes data within the sensor itself, eliminating the need for an external controller or software. It automatically sets baseline rhythms and alert thresholds, allowing users to monitor machine health without requiring specialized vibration expertise.
A scalar value is a single number that expresses the magnitude of a measurable property, without any directional component. It tells you how much of something, but not where it’s going—like °F for temperature or km/h for speed. By contrast, a vector has both magnitude and direction—like velocity; for example, 30 m/s to the east. In signal analysis, scalar values are used to simplify complex waveforms into meaningful metrics. RMS returns a scalar representing average amplitude over time, while FFT produces a series of scalar values representing signal amplitude across distinct frequency bands.
RMS (Root Mean Square) is a method, and a resulting scalar value, for calculating the average amplitude of a signal over time. It captures the full contribution of both positive and negative values, which carry amplitude regardless of sign. This contrasts with frequency-domain methods like FFT, which measure how much amplitude is present at each frequency. In signal analysis—whether it’s mechanical vibration, acoustic pressure, or electrical oscillation—RMS gives you a meaningful measure of a signal’s amplitude over time. In the case of sound, RMS is used to calculate values expressed on a dB scale, which relate to perceived loudness. In mechanical systems, RMS reflects how much force, displacement, velocity, or acceleration is being delivered on average—typically expressed in units like millimeters per second (mm/s) or meters per second squared (m/s²), depending on what’s being measured. These values are useful for assessing wear, stress, or overall performance—unlike peak values, which only capture brief extremes.
FFT (Fast Fourier Transform) is a method for analyzing a signal—such as vibration, sound, or voltage—by breaking it down into its frequency components. It transforms a time-based signal into a representation that shows how much energy is present within narrow frequency ranges (i.e., bins). The result is a series of scalar values indicating the amplitude in each frequency interval. FFT is used to analyze all kinds of signals—mechanical vibration, audio, neurological—anywhere insight is found in the frequency domain.
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