KNOWLEDGE HUB · UPDATED WEEKDAYS
Reference material on AI predictive maintenance (PdM) and equipment health management (PHM) — benchmark reports, translated research, field-data insights and a daily industry brief — compiled by JinBond with DHMSCare (灵瞳) for manufacturers in pharma & food, packaging & printing, process industries and beyond. Free to consult and cite for internal business cases.
Most of our long-form reports are written for a Chinese-speaking audience; each entry below is labelled so you know what language to expect before you click.
Long-form reading for decision-makers: benchmarks, field guides, data snapshots and failure lessons.
A horizontal comparison of mainstream AI PdM offerings across capability dimensions, industry fit and deployment maturity — intended as a citable baseline for supplier selection and internal project approval.
Published by IIoT-World: technology paths for AI predictive maintenance, how ROI is actually calculated, and a phased implementation roadmap. This is the original English article; we also host an authorised Chinese translation.
Focused on power, water and petrochemical assets, this report reads the adoption status and trends of AI predictive maintenance through industry data published in July 2026.
Evidence over concepts: a checklist of real manufacturing AI use cases, an ROI method you can recompute yourself, and four recurring reasons these projects fail.
What the system does, how alerts are sent and to whom, how the mobile app and the web console divide the work, and how hard it is to get started — 18 questions answered. Published in Chinese.
The English edition of the Q&A above: what the system does, its six core modules, how alerts are sent and to whom, how the app and the web console divide the work, permissions, and how hard it is to get started — 18 questions answered in English.
How to read a fault from a vibration spectrum: a 13-row signature table (1X/2X, BPFO/BPFI/BSF/FTF, GMF), the three keys to reading a spectrum, five measurement parameters and their lead time, the P-F curve, sensor configuration by equipment type, and three common defects that make sensors useless. Published in Chinese.
Chemical plants run the deepest DCS base in manufacturing — what is missing is the mechanical side and how to fuse the two. Two data maps (what DCS already has / what must be added), shaft vs casing vibration, virtual sensors such as NPSHa-based pump cavitation early warning, read-only one-way data access, and the three-way root cause method. Published in Chinese.
One table covering the spectral signature, phase behaviour and sensitive parameter of 13 typical rotating-equipment faults — unbalance, misalignment, looseness, bearing outer/inner/rolling-element/cage defects, gear wear and tooth breakage, resonance, oil whirl, blade pass frequency and shaft bow — plus how BPFO/BPFI/BSF/FTF/GMF are defined and calculated. Published in Chinese.
In dosage plants the target is not "is the machine broken" but "has precision drifted" — because fill weight, hardness and fill volume are quality attributes set by machine accuracy. Covers five data streams, six point classes inside machine PLCs plus three ways out of closed interfaces, what must be added externally, sensor configuration for four solid-dosage and five aseptic equipment types, the machine-state-to-quality-attribute map, dust explosion zoning and OEB containment, cleanroom HVAC red lines, read-only validated data access, and batch binding with three-way root cause analysis. Published in Chinese.
API plants add one layer of explosion-proof and wash-down constraints over fine chemicals, and one layer of batch and deviation traceability over dosage forms. Covers how to build a GMP-relevance classification ledger (and why QA must co-sign), four data streams, six point classes in DCS and batch systems, what must be added externally, monitoring configuration for agitators, centrifuges and vacuum pumps, utility and cleanroom red lines, read-only validated access, batch binding and three-way root cause analysis. Published in Chinese.
For breweries, beverage and dairy plants the first question is hygiene, loss and line rate — not "is the bearing worn". Covers the mandatory hygienic-design constraints (GB 16798 material and construction clauses) and four hard installation rules, five data streams and how to work around a packaging line built from multiple vendors, eight equipment types that need external sensors, CIP monitoring via the four parameters and return-line verification, whole-line rate and four categories of product loss, ammonia refrigeration safety and fouling on the thermal side, the three non-vibration degradation modes (fouling, blockage, leakage), read-only access and the line-rate dimension. Published in Chinese.
A reading of the arXiv preprint "When LLMs Sit Above Diagnostic Tools: Unrealized Complementarity in Industrial Fault Diagnosis": stacking an LLM on top of diagnostic tools does not automatically produce a better system. On the held-out evaluation the expert alone scored 83.33%, implicit LLM-expert integration 77.43%, unaided LLM 64.67%, and the two-source selector oracle 92.76%. The dominant failure mode is missing corrections (140 rows) rather than accepting bad advice (15 rows). Task-aligned physical evidence helped; raising reasoning effort did not. Includes the experimental design, five practical takeaways and the study boundaries. Published in Chinese.
A translation and summary of the Euro Bearing Blog analysis "How AI Is Replacing Manual Vibration Analysis in 2026". Manual route-based vibration testing costs roughly EUR 30-50 per machine per month; an AI condition-monitoring programme falls to EUR 2-5 once the upfront investment is amortised - about an order of magnitude apart. Covers what a vibration analyst actually does in a day, the seven-stage AI pipeline (envelope demodulation, order tracking, characteristic frequencies, harmonic recognition, anomaly detection, severity grading, recommended action), the four structural advantages of AI, the three areas humans still own (novel failure modes, mechanical context, root cause analysis), a five-step 2026 rollout, the shift from predictive to prescriptive output, and what the cost inflection means for the analyst skill mix. Published in Chinese.
Every working day we select 3–5 developments in AI for industrial use, weighted towards equipment predictive maintenance (PHM/PdM), each with a short note on why it matters. Published in Chinese — issue numbers below use MM-DD.
The terms that come up in almost every predictive-maintenance conversation, defined the way we use them on this site.
Maintaining equipment based on its measured condition rather than the calendar. Sensors and models indicate that a fault is developing, and the repair is scheduled before the failure.
The broader discipline PdM sits inside: assessing current health, estimating remaining useful life, and feeding that into maintenance planning.
Continuous or periodic measurement of physical parameters — most often vibration and temperature — to track how far a machine has drifted from its healthy baseline.
A time waveform converted by Fast Fourier Transform into amplitude versus frequency. Because bearing wear, gear damage and unbalance each show up at characteristic frequencies, the spectrum is what makes root-cause identification possible.
A technique for isolating the repetitive impact pulses produced by a localised bearing or gear defect. It typically reveals damage earlier than a standard velocity spectrum.
The recorded vibration signature of a machine while it is healthy and running normally. Every later reading is judged against it — which is why most projects need several months of data before alerts become trustworthy.
A single overall value, usually velocity RMS in mm/s, compared against ISO severity zones. Good for general alarms and trending — it does not, on its own, tell you which part is failing.
The share of alerts that turn out not to indicate a real problem. It is the metric most predictive-maintenance programmes actually fail on: too many false alarms and the maintenance team stops looking at the alerts at all.
We can rank your equipment by downtime cost and failure risk, propose first-wave monitoring points, and put numbers to the business case with our ROI estimator.
Direct line: 13827422637 · Open the ROI calculator →
