{"id":20602,"date":"2025-10-31T00:25:51","date_gmt":"2025-10-31T00:25:51","guid":{"rendered":"https:\/\/vibromera.eu\/glossary\/iso-13374\/"},"modified":"2026-07-03T10:03:40","modified_gmt":"2026-07-03T10:03:40","slug":"iso-13374","status":"publish","type":"glossary","link":"https:\/\/vibromera.eu\/km\/glossary\/iso-13374\/","title":{"rendered":"ISO 13374: Data Processing and Communication for Monitoring"},"content":{"rendered":"

ISO 13374: Condition Monitoring and Diagnostics of Machines \u2014 Data Processing, Communication and Presentation<\/h1>\n\t\t
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\u20ac<\/span>90.00<\/span> + VAT (if applicable)<\/h4><\/div>\n\t\t\t\t\t\t\t\t\t
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\u20ac<\/span>1,735.00<\/span> + VAT (if applicable)<\/h4><\/div>\n\t\t\t\t\t\t\t\t\t
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ISO 13374<\/strong> is one of the most influential standards in industrial IoT and condition monitoring<\/a> software. Rather than defining how to take a measurement, it tackles a different problem entirely: interoperability<\/em> \u2014 how data from different sensors, acquisition hardware, and analysis platforms can flow together without proprietary barriers. It specifies a standardised, open architecture for how condition-monitoring data should be processed, stored, and exchanged, and it is closely tied to the Machinery Information Management Open Systems Alliance (MIMOSA) architecture on which it is built. The goal is a “plug-and-play” environment for condition-monitoring technology, and the heart of the standard is a six-block functional model that traces the journey from a raw sensor signal to a clear maintenance recommendation.<\/p>\n

1. Summary: What ISO 13374 Sets Out to Do<\/h2>\n

Where measurement-oriented standards tell you what<\/em> to measure and against what limit, ISO 13374 governs how the information moves and is structured<\/em> once it has been captured. It complements the measurement and procedural standards rather than competing with them: a vibration severity standard such as ISO 20816-1<\/a> (the modern successor to ISO 10816) supplies the alarm thresholds, the general monitoring standard ISO 13373-1<\/a> describes the vibration-monitoring procedure, and the overarching ISO 17359<\/a> sets out the general condition-monitoring strategy \u2014 while ISO 13374 defines the open data architecture that carries the results between systems. The standard is published in several parts and describes a layered information architecture; its core is a functional block diagram with six key layers representing the data flow in any condition-monitoring system.<\/p>\n

2. The Six Functional Blocks<\/h2>\n

The model is best read as a pipeline. Each block consumes the output of the one before it and produces something more refined \u2014 from raw volts at the bottom to an actionable advisory at the top.<\/p>\n

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  1. \n 1. DA \u2014 Data Acquisition Block:<\/strong><\/p>\n

    This is the foundational layer, the bridge between the physical machine and the digital monitoring system. The DA block interfaces directly with sensors \u2014 such as accelerometers<\/a>, proximity probes<\/a>, temperature sensors<\/a>, or pressure transducers \u2014 and acquires the raw, unprocessed analogue or digital signals they produce. It is responsible for all low-level hardware interactions: providing power to the sensors (for example IEPE power for accelerometers), performing signal conditioning such as amplification and filtering to remove unwanted noise, and executing the analogue-to-digital conversion (ADC). Its output is a digitised stream of raw data \u2014 typically a time waveform<\/a> \u2014 passed up to the next layer.<\/p>\n<\/li>\n

  2. \n 2. DM \u2014 Data Manipulation Block:<\/strong><\/p>\n

    This is the computational engine of the monitoring system. It receives the raw, digitised stream (for instance the time waveform) from the DA block and transforms it into more meaningful data types suited to analysis. Its core function is standardised signal processing \u2014 most notably the Fast Fourier Transform (FFT)<\/a>, which converts the time-domain signal into a frequency-domain spectrum<\/a>. Other tasks defined within this block include calculating broadband metrics such as overall RMS<\/a> values, performing digital integration to convert acceleration to velocity<\/a> or displacement<\/a>, and running more advanced processes such as demodulation<\/a> or envelope analysis<\/a> for detecting the tell-tale high-frequency impacts of rolling-element bearing faults.<\/p>\n<\/li>\n

  3. \n 3. SD \u2014 State Detection Block:<\/strong><\/p>\n

    This block marks the critical transition from data manipulation to automated state detection. It takes the processed data from the DM block (RMS values, specific frequency amplitudes, spectral bands) and applies logical rules to determine the machine’s operational state \u2014 this is where a problem is first “detected.” Its primary function is threshold checking: it compares the measured values against pre-defined alarm setpoints, such as the zone boundaries defined in ISO 20816 (formerly ISO 10816) or user-defined percentage changes from a baseline<\/a>. On that basis it assigns a discrete state to the data \u2014 “Normal,” “Acceptable,” “Alert,” or “Danger” \u2014 turning raw numbers into actionable information that can be passed up for diagnosis or used to trigger an immediate alarm<\/a>.<\/p>\n<\/li>\n

  4. \n 4. HA \u2014 Health Assessment Block:<\/strong><\/p>\n

    This block functions as the “brain” of the diagnostic system, answering the question, “What is the problem?” It receives the state information (say, an “Alert” status) from the SD block, together with the processed data from the DM block, and applies analytical intelligence to find the specific root cause of the anomaly. Here the diagnostic logic runs \u2014 anything from simple rule-based systems to complex artificial-intelligence algorithms. For example, if the SD block flags high vibration at a frequency exactly twice the shaft’s running speed (2X), the rule-based logic would correlate that pattern and output a diagnosis of “probable shaft misalignment<\/a>.” If the alert is on a non-synchronous, high-frequency peak with characteristic sidebands<\/a>, it would diagnose a specific bearing defect<\/a>. The output is a concrete health assessment for the machine component.<\/p>\n<\/li>\n

  5. \n 5. PA \u2014 Prognostic Assessment Block:<\/strong><\/p>\n

    This block represents the pinnacle of predictive maintenance<\/a>, aiming to answer the crucial question, “How much longer can it run safely?” It takes the specific fault diagnosis from the HA block and combines it with historical trend<\/a> data to forecast how the fault will progress. This is the most complex layer, often employing machine-learning models or physics-of-failure models to extrapolate the current rate of degradation and estimate the Remaining Useful Life (RUL)<\/a> of the component. If the HA block identifies a bearing defect, the PA block analyses how quickly the defect frequencies have grown over recent months to predict when they will reach a critical level. The output is not just a diagnosis but a timeframe for action \u2014 the realm of prognosis<\/a>.<\/p>\n<\/li>\n

  6. \n 6. AG \u2014 Advisory Generation Block:<\/strong><\/p>\n

    This is the final and, from the user’s perspective, most critical layer, because it translates all the underlying data and analysis into actionable intelligence. The AG block communicates the findings of the lower layers to operators, reliability engineers, and maintenance planners \u2014 presenting the right information to the right person in the right format. That can mean intuitive dashboards with colour-coded health indicators, automatically generated email or text alerts, or detailed diagnostic reports<\/a> with spectral and waveform plots, and, above all, clear maintenance recommendations. An effective AG block does not merely state that a bearing has a fault; it provides a full advisory, such as: “Inner-race defect detected on motor outboard bearing. Remaining useful life estimated at 45 days. Recommendation: schedule bearing replacement at the next planned shutdown.”<\/p>\n<\/li>\n<\/ol>\n

    3. Key Concepts<\/h2>\n