{"id":5757,"date":"2026-09-15T11:33:30","date_gmt":"2026-09-15T03:33:30","guid":{"rendered":"https:\/\/prognosistech.com\/?page_id=5757"},"modified":"2026-09-30T14:18:49","modified_gmt":"2026-09-30T06:18:49","slug":"fft","status":"publish","type":"page","link":"https:\/\/prognosistech.com\/en\/fft\/","title":{"rendered":"FT Spectrum Analysis and Fault Diagnosis"},"content":{"rendered":"
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What Is FFT Spectrum Analysis?
How Vibration Spectra Help Identify Equipment Faults<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Vibration monitoring helps detect abnormal equipment behavior, while FFT spectrum analysis takes the next step by helping identify what may be causing the abnormal vibration.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Industrial machinery naturally generates vibration during operation.<\/p>

With vibration sensors, we can collect vibration signals from operating equipment. However, simply knowing that \u201cvibration has increased\u201d does not necessarily tell us what is actually wrong with the machine.<\/p>

This is where FFT spectrum analysis becomes valuable. By analyzing the frequency components within a vibration signal, FFT can reveal characteristic patterns that may be associated with specific equipment faults.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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What Is FFT Spectrum Analysis?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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FFT stands for Fast Fourier Transform.<\/p>

It is a mathematical technique that converts vibration signals from the time domain into frequency-domain information.<\/p>

For example, after a vibration sensor collects data from a piece of equipment, the original signal shows:<\/p>

How the equipment\u2019s vibration changes over time<\/strong><\/p>

After FFT analysis, the signal can be viewed as:<\/p>

Which frequencies exhibit more significant vibration<\/strong><\/p>

By analyzing these frequency characteristics, engineers can compare them with information such as the equipment\u2019s rotational speed, bearings, gears, and other components. This helps identify abnormal vibration patterns and diagnose potential equipment faults.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Why Is FFT Analysis Necessary?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Suppose a motor is experiencing abnormal vibration. Simply observing an increase in vibration levels only tells us:\n\n\u201cThere may be a problem with this motor.\u201d<\/p>\n

Possible causes may include:<\/p>\n

    \n
  • \n
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    • Rotor imbalance<\/li>\n
    • Shaft misalignment<\/li>\n
    • Bearing Degradation<\/li>\n
    • Structural Looseness<\/li>\n
    • Gear Defects<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

      The value of FFT lies in its ability to break down complex vibration signals into different frequency components, allowing us to further identify:<\/p>\n

      \n

      \u201cAt which frequencies is the equipment exhibiting abnormal vibration?\u201d<\/strong><\/p>\n<\/blockquote>\n

      By analyzing these frequencies against known fault signatures of the equipment, we can identify potential fault causes and support more accurate equipment fault diagnosis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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      How to Read an FFT Spectrum: Understanding the Two Key Axes<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      Reading an FFT spectrum is not as complicated as it may seem. The key is to understand the relationship between the X-axis and Y-axis:<\/p>

      • X-axis (Frequency, Hz): Identify Potential Fault Frequencies<\/b><\/p>

        • Represents the frequency at which vibration occurs. The fundamental rotational frequency, or 1X, can be calculated by dividing the equipment\u2019s rotational speed (RPM) by 60.\nExample: 1,800 RPM \u00f7 60 = 30 Hz (1X).\nIf a significant energy peak appears at 30 Hz, it indicates that the vibration is directly related to the shaft\u2019s rotational frequency.<\/p><\/li><\/ul><\/li>

        • Y-axis (Amplitude): Assess Vibration Severity<\/b><\/p>

          • Represents the vibration intensity at a given frequency. The higher the peak, the greater the vibration energy at that frequency.<\/p><\/li><\/ul><\/li><\/ul>

            However, one important point to keep in mind:<\/p>

            A single peak does not necessarily indicate a specific type of fault.<\/p><\/blockquote>

            Accurate fault diagnosis requires the frequency spectrum to be evaluated together with the equipment\u2019s rotational speed, measurement location, machine structure, and other vibration characteristics.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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            Is Vibration Data Enough for Fault Diagnosis?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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            Not necessarily.<\/strong><\/p>

            This is one of the most important concepts in equipment vibration analysis.<\/p>

            Today, collecting vibration data from equipment is relatively easy. However:<\/p>

            Having data \u2260 Having diagnostic capability.<\/strong><\/p>

            For example:<\/p>

            \u201cThe equipment\u2019s vibration level has increased.\u201d<\/p>

            This only tells us that the equipment may be experiencing an abnormal condition.<\/p>

            The real question is:<\/p>

            \u201cWhy has the vibration increased?\u201d<\/strong><\/p>

            Answering this question requires further analysis of the vibration signals and their associated fault characteristics.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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            From Vibration Monitoring to Intelligent Fault Diagnosis<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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            For industrial equipment, the real value lies not simply in collecting more data, but in turning that data into actionable insights about equipment condition.<\/p>

            FFT spectrum analysis can identify frequency characteristics from complex vibration signals. When combined with fault databases and intelligent analytics, it can further support equipment fault diagnosis.<\/p>

            As a result, equipment health management can move beyond simply asking:\n\n\u201cIs there an abnormality?\u201d\n\nto:\n\n\u201cWhat type of fault could be causing it?\u201d\n\nand ultimately help maintenance personnel determine:\n\n\u201cWhat should we inspect first?\u201d<\/p>

            This represents an important step in the evolution of vibration monitoring\u2014from simple data collection to intelligent fault diagnosis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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            FFT Spectrum Analysis is an essential tool for vibration analysis and equipment fault diagnosis.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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            By converting vibration signals into frequency-domain information, FFT spectrum analysis allows us to observe vibration patterns such as rotational frequencies, bearing fault frequencies, and gear mesh frequencies, helping identify potential abnormalities in industrial equipment.<\/p>

            However, true fault diagnosis is not simply about \u201clooking at an FFT spectrum.\u201d<\/p>

            Vibration signals + spectrum analysis + equipment information + extensive fault cases are what enable equipment data to be transformed into meaningful and actionable diagnostic insights.<\/p>

            Through vibration monitoring and an extensive equipment fault database, Prognosis Technology helps businesses move from detecting abnormalities and analyzing vibration characteristics to identifying potential equipment faults, enabling a more proactive approach to equipment maintenance rather than simply waiting for failures to occur.<\/p>

            From monitoring vibration to truly understanding your equipment.<\/strong><\/p><\/blockquote>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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            <\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

            FFT \u983b\u8b5c\u5206\u6790\u662f\u4ec0\u9ebc\uff1f\u5982\u4f55\u5229\u7528\u632f\u52d5\u983b\u8b5c […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_monsterinsights_skip_tracking":false,"_templately_pack_id":"","_templately_imported_at":"","_templately_source":"","_templately_import_session_id":"","site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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\u983b\u8b5c\u5206\u6790\u662f\u4ec0\u9ebc\uff1f\u5982\u4f55\u5229\u7528\u632f\u52d5\u983b\u8b5c\u627e\u51fa\u8a2d\u5099\u6545\u969c -\u666e\u683c\u8afe\u65af\u79d1\u6280Prognosis Technology<\/title>\n<meta name=\"description\" content=\"FFT \u983b\u8b5c\u5206\u6790\u662f\u8a2d\u5099\u632f\u52d5\u6545\u969c\u8a3a\u65b7\u7684\u91cd\u8981\u5de5\u5177\u3002\u4e86\u89e3\u6642\u57df\u8207\u983b\u57df\u5206\u6790\uff0c\u638c\u63e1\u632f\u52d5\u983b\u8b5c\u5224\u8b80\u65b9\u5f0f\uff0c\u4ee5\u53ca\u5982\u4f55\u5f9e\u983b\u7387\u7279\u5fb5\u5354\u52a9\u8fa8\u8b58\u99ac\u9054\u3001\u8ef8\u627f\u3001\u9f52\u8f2a\u7b49\u8a2d\u5099\u7570\u5e38\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/prognosistech.com\/en\/fft\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"FFT 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