Reliability
Remaining useful life (RUL): report an interval, not one number
Learn how RUL is defined, what each of three common approaches needs as data, and how to report P10, P50 and P90 values with a worked trend calculation.
Engineering knowledge.With a way to try it.
Sourced method notes, with the tools to try them.
ML
Isolation Forest for sensor anomalies: how it works and where it misleads
Read flagged rows in a sensor table by their isolation score, understand what contamination does in the model, and see where the method misleads you.
ML
Multivariate process monitoring with Hotelling T² and SPE
Read correlated sensors through T² and SPE together rather than one statistic, interpret contribution plots, and learn where the method’s assumptions break.
Instrumentation
How to read an FFT spectrum: resolution, windows, peaks
After reading, you can compute frequency resolution for a vibration record, justify your window choice, and read spectral peaks as clues rather than diagnoses, using a worked example.
Instrumentation
Envelope analysis for rolling bearing faults: steps and frequencies
This article shows how envelope analysis pulls repeating impacts out of a high-frequency vibration band, how to calculate the four defect frequencies, and how to read the result step by step.
ML
Without fault labels: unsupervised machine learning in industry
Learn what unlabelled sensor history can and cannot tell you, how to evaluate a detector without labels, and why operating modes need attention first.
Reliability
Kalman filtering: a smoothed health indicator from a noisy sensor
Learn the predict and update steps, what the gain K means, and how a level-and-slope state yields a smoothed health indicator together with its rate of change.
Quality
CUSUM and EWMA: catching small, sustained shifts
Follow how CUSUM and EWMA accumulate small process shifts, work through a six-point example, and choose their parameters with false alarms in mind.
Instrumentation
Nyquist and aliasing: how fast to sample sensor data
After reading, you can set a sampling rate from your analysis band, recognise a false low-frequency peak caused by aliasing, and see why slow PLC or SCADA logs fall short for spectra.
Instrumentation
ISO 20816 vibration severity zones: how to use them and where not to
After reading, you can track a machine's broadband vibration against its own baseline and read the ISO 20816 zones in the right context, without treating a zone as a fault diagnosis.
Quality
Process capability indices: reading Cp and Cpk correctly
Work through Cp and Cpk on a hypothetical example, see how centring changes the result, and learn the conditions that must hold before the indices can be interpreted.
Reliability
MTBF, MTTR and Weibull: reading reliability metrics correctly
Compute MTBF, MTTR and availability from a hypothetical record, then see why MTBF is not a lifetime and how the Weibull shape parameter β bears on scheduled replacement.
Control
PLC scan cycle and data sampling error
Before reading a PLC tag for AI, know where in the scan cycle the value was produced and which timestamp describes it.
Networking
Modbus register maps: scaling and byte order
A number read over Modbus is not an engineering value until address base, scale and word order are verified.
OT
OPC UA security: modes, certificates and trust lists
Do not leave an OPC UA connection at “it works”. Treat security mode, certificate trust and user authentication as separate decisions.
DataOps
How historian compression distorts machine learning
The series in a historian is not the series the sensor saw. Compression settings change what features and models learn.
ML
SPC control charts and anomaly detection: same question, different language
Control charts have watched process drift for decades. Before proposing a machine learning model, look at what this simple method says.
Instrumentation
Vibration analysis basics: velocity, acceleration and spectrum
Data from a vibration sensor cannot be interpreted without knowing which quantity it measures, which frequency range it sees and how it was mounted.
Control
Alarm management: fix the alarm system before adding AI
Alarm floods are solved with an alarm philosophy first, not a model. AI works better on an orderly alarm list.
Networking
MQTT and Sparkplug: a shared contract for industrial data
MQTT carries messages but does not define a data model. Sparkplug fills that gap by standardizing topic names and payload structure.
ML
Baselines in forecasting projects: why the naive method must be beaten
A forecasting model does not earn its complexity unless it measurably beats a method as simple as “repeat the last value”.
RAG
A RAG chunking strategy for technical manuals
Splitting a manual into arbitrary-length pieces makes the right answer get searched in the wrong context. Preserve headings, tables and revision information.
ML
Prevent leakage in industrial time series
Why randomly splitting sensor rows can exaggerate real-world performance.
OT
Define the access boundary of an OT AI pilot
A starting architecture that separates reading, recommendations and control authority.
DataOps
Prepare OPC UA data for AI
Turn a tag value into a usable observation with units, quality, timestamps and asset context.
DataOps
Check data quality before calculating OEE
Availability, performance and quality must use consistent production boundaries.
ML
Build a predictive maintenance pilot around a decision
Connect sensor behavior to operating context, maintenance events and a false-alarm budget.
ML
Design a dataset for visual quality inspection
Track products, batches, lighting and label consistency before counting defect images.
RAG
Industrial RAG: how to measure answer quality
Evaluate answers from engineering manuals with source relevance, device context and appropriate abstention.
R&D
A one-page brief for an industrial AI pilot
Put the business decision, data owner, baseline and acceptance criteria in one place.
Edge AI
Browser analytics with C++ and WebAssembly
Compute locally with a small numerical kernel, explicit memory limits and reproducible checks.
ML
Alert thresholds, precision and recall
Why a single accuracy percentage can hide failure on rare events.
Edge AI
Machine Learning in 21 KB: Our WebAssembly Kernel
Kutup moved its client-side machine learning into a 21 KB WebAssembly kernel written in C — no TensorFlow.js, no 50 MB model download. How it works and what it buys.
DataOps
Time-Series Data in Industry: Storage, Trends, and Forecasting
From historians to machine learning, time-series data powers industrial analytics. A practical guide to collection, quality, and the forecasting methods that actually work on plant data.
RAG
Making LLMs Reliable for Industrial Q&A: Evidence-First Design
General chatbots hallucinate; industrial answers must be verifiable. This article describes the evidence-first design pattern: curated corpora, hybrid retrieval, constrained synthesis, and inspectable output.
Instrumentation
Instrument Calibration: Zero, Span, and Uncertainty
Calibration is how a measurement earns trust. The fundamentals — zero, span, linearity, hysteresis, and uncertainty — every engineer should apply before relying on an instrument.
RAG
Word Embeddings and Semantic Search: How Modern Retrieval Works
Semantic search matches meaning, not spelling. This article explains embeddings, vector similarity, and why hybrid keyword-plus-vector retrieval is the robust production pattern.
DataOps
What Is a Data Historian? Time-Series Data in Process Plants
Process data is time-series data, and general-purpose databases handle it poorly. Historians are purpose-built for collecting, compressing, and serving millions of process values.
Instrumentation
The 4–20 mA Signal: Why Analog Measurement Endures
Decades after digital fieldbuses arrived, the 4–20 mA loop still dominates instrumentation. The reasons are practical, not nostalgic: wire break detection, distance, and noise immunity.
Control
What Is SCADA? Architecture and Components Explained
SCADA systems supervise and control geographically distributed assets. A practical breakdown of their five core layers and how they fit together.
OT
IEC 62443 and OT Security: Zones, Conduits, and Best Practices
IEC 62443 is the reference standard for industrial automation and control system security. Here is how its zone and conduit model translates into concrete network decisions.
RAG
RAG vs Fine-Tuning: Building Domain-Specific LLM Assistants
Retrieval-augmented generation and fine-tuning solve different problems. Knowing which to use — and why most production systems need both — prevents expensive mistakes.
Networking
PROFINET, EtherNet/IP, and EtherCAT: Comparing Industrial Ethernet
Three standards dominate industrial Ethernet. They share the same cables and switches — but their timing, topology, and engineering models differ in ways that matter.
DataOps
SQL Normalization: Why Database Design Still Matters
Normalization is the discipline of designing relational schemas that stay consistent as data grows. A practical guide to the normal forms that matter for real systems.
Control
PID Control: How Proportional, Integral, and Derivative Terms Work
PID is the workhorse of process control. Understand what each term actually does, how the controller responds, and what tuning really means before you touch a loop.
Networking
What Is OPC UA? A Practical Introduction for Engineers
OPC UA is the modern standard for industrial data exchange — secure, information-rich, and platform-independent. Here is what it actually provides and how it fits your architecture.
Control
PLC vs DCS: How to Choose the Right Control Architecture
Programmable logic controllers and distributed control systems both automate industrial processes — but they were designed for different problems. Here is how engineers choose between them.
Networking
Modbus TCP vs Modbus RTU: Choosing the Right Protocol
Modbus remains the most widely supported industrial protocol in the world. This guide compares the serial RTU variant with the Ethernet TCP variant and explains when each belongs in your architecture.
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