ISO 13374: Condition Monitoring and Diagnostics of Machines — Data Processing, Communication and Presentation

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ISO 13374 is one of the most influential standards in industrial IoT and condition monitoring software. Rather than defining how to take a measurement, it tackles a different problem entirely: interoperability — 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.

1. Summary: What ISO 13374 Sets Out to Do

Where measurement-oriented standards tell you what to measure and against what limit, ISO 13374 governs how the information moves and is structured 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 (the modern successor to ISO 10816) supplies the alarm thresholds, the general monitoring standard ISO 13373-1 describes the vibration-monitoring procedure, and the overarching ISO 17359 sets out the general condition-monitoring strategy — 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.

2. The Six Functional Blocks

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

1. 1. DA — Data Acquisition Block:

   This is the foundational layer, the bridge between the physical machine and the digital monitoring system. The DA block interfaces directly with sensors — such as accelerometers, proximity probes, temperature sensors, or pressure transducers — 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 — typically a time waveform — passed up to the next layer.

2. 2. DM — Data Manipulation Block:

   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 — most notably the Fast Fourier Transform (FFT), which converts the time-domain signal into a frequency-domain spectrum. Other tasks defined within this block include calculating broadband metrics such as overall RMS values, performing digital integration to convert acceleration to velocity or displacement, and running more advanced processes such as demodulation or envelope analysis for detecting the tell-tale high-frequency impacts of rolling-element bearing faults.

3. 3. SD — State Detection Block:

   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 — 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. On that basis it assigns a discrete state to the data — “Normal,” “Acceptable,” “Alert,” or “Danger” — turning raw numbers into actionable information that can be passed up for diagnosis or used to trigger an immediate alarm.

4. 4. HA — Health Assessment Block:

   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 DM block and applies analytical intelligence to find the specific root cause of the anomaly. Here the diagnostic logic runs — anything from simple rule-based systems to complex artificial-intelligence algorithms. For example, if the DM 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.” If the alert is on a non-synchronous, high-frequency peak with characteristic sidebands, it would diagnose a specific bearing defect. The output is a concrete health assessment for the machine component.

5. 5. PA — Prognostic Assessment Block:

   This block represents the pinnacle of predictive maintenance, 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 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) 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 — the realm of prognosis.

6. 6. AG — Advisory Generation Block:

   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 — 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 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.”

3. Key Concepts

• Interoperability: the primary goal of ISO 13374. By defining a common framework and data model, it lets a company use sensors from Vendor A, a data-acquisition system from Vendor B, and analysis software from Vendor C, and have them all work together.
• Open architecture: the standard promotes open, non-proprietary protocols and data formats, preventing vendor lock-in and fostering innovation across the condition-monitoring industry.
• MIMOSA: the standard is heavily based on the work of the MIMOSA organisation. Understanding MIMOSA’s C-COM (Common Conceptual Object Model) is key to understanding the detailed implementation of ISO 13374.
• From data to decisions: the six-block model provides a logical pathway from raw sensor measurements (Data Acquisition) to actionable maintenance advice (Advisory Generation), forming the digital backbone of a modern predictive-maintenance programme and a natural foundation for condition-based maintenance.

4. Where the Standard Fits in Practice

ISO 13374 is deliberately silent on instruments and thresholds, which is exactly what makes it powerful: it lets the rest of the toolchain evolve independently. In a typical reliability programme it sits alongside the standards that define what is measured and how severe the result is. The threshold values that feed the SD block come from severity standards and from your own baselines; the prognostic models in the PA block draw on the data the architecture has faithfully preserved. Practical aids slot neatly into this picture — a condition-monitoring parameters calculator helps set the alarm and danger thresholds the SD block will apply, a condition-monitoring method selector helps choose the techniques the DA and DM blocks will implement, and an RUL prognostics calculator mirrors the work of the PA block in estimating remaining life. For online deployments, the same six-block flow underlies online monitoring systems and the telemetry that carries their data.

5. The Field Instrument at the Bottom of the Stack

Every layer of ISO 13374 ultimately depends on trustworthy raw data from the DA and DP blocks — if the acquisition or processing is poor, no amount of clever prognostics will save the conclusion. This is where a capable field instrument earns its place. A portable two-channel analyser such as the Balanset-1A performs the DA and DM roles in a single hand-held package: it powers and reads its accelerometers, captures the time waveform, computes the FFT spectrum and overall RMS, and presents the result for state detection. When a machine flagged at the DM or HA layer turns out to suffer from unbalance, the same instrument closes the loop by field-balancing the rotor in its own bearings — a reminder that the data architecture exists to drive real corrective action on the shop floor, not just to populate a dashboard.

6. The Official Standard

ISO 13374 is published in multiple parts by the International Organization for Standardization, with the general-guidelines part establishing the functional blocks and later parts addressing data processing and the presentation of processed data. The authoritative, complete text — including the formal definitions of each block and the associated data model — is available for purchase through the official ISO Store, where the standard is listed under its ISO reference number. The summary above is intended to be self-contained for day-to-day engineering use, but the published standard remains the definitive source for compliance and detailed implementation.

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