Railway Bogie Condition Monitoring Patents: Trends & Filing Gaps 2026
- A short, sharp filing burst. Annual filings climbed to a peak of 6 in 2023 from just 2 the year before, rather than building gradually since 2015.
- Testing and measurement claims dominate. G01M covers 11 of 14 records, well ahead of running-gear structure claims under B61F.
- AI-inference and velocity-sensing claims are nearly untouched. G01P and G06N each hold just a single record, against a citation-leading cluster of wheel- and axle-mounted sensor patents.
Top-5 share is the combined record count of the five largest assignees divided by all 14 records in scope (CR5), not by the ranked leaders only.
What this patent landscape covers
Railway bogie condition monitoring covers apparatus and methods for detecting wear, damage or anomalous behaviour in a railcar’s wheelset, axle, damper or broader bogie assembly, typically through onboard sensors feeding a diagnostic or predictive-maintenance process. This dataset draws on 14 published patent families filed between 2015 and a partial 2026, filtered to records combining bogie or rail-truck terminology with condition-monitoring language under the relevant testing, running-gear and traffic-control IPC codes.
The corpus is small enough that a handful of filers and a handful of IPC subclasses account for most of the activity. Reading the trend line, the IPC split and the citation table together shows not just what has been filed, but which parts of the problem, sensor hardware, inference software, or traffic-integration layers, remain thinly claimed.
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Filing trend and technology composition
Fourteen published families sit inside this search string, spanning 2015 through the partial 2026 year. The pattern is one of a short, sharp filing burst rather than a steadily building field.
Filings peaked in 2023, then eased
Annual filings rose from zero in 2017 to a peak of 6 in 2023. With 2022 sitting at 2, the trajectory into the peak was steep and short-lived rather than a long build-up, and the most recent year is still partial under the 18-month publication lag.
Testing and measurement claims dominate the IPC mix
G01M (testing machine and structure balance) covers 11 of the 14 records, well ahead of B61K auxiliary equipment (8) and B61F running gear and bogies (6). B61L traffic control, G01P velocity/acceleration and G06N AI-model computing each appear only once or a handful of times, marking them as the thinner, less-claimed branches of the technology.
Shares are the percentage of the 14 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Railway Bogie Condition Monitoring with Eureka
This page is one run against one query. Ask Eureka your own question about railway bogie condition monitoring and every answer comes back with the patent numbers behind it.
Try EurekaA representative filing and the most-cited records
WO2023152699A1 — Apparatus and method for monitoring a railway, subway or tramway bogie
The invention relates to an apparatus and a method for monitoring a railway, subway or tramway bogie comprising a damper, wherein said method comprises an acquisition phase where a travel datum representing bogie behaviour in a travelling condition is acquired, a processing phase where a neural network determines a monitoring datum representing the damper's operating state from that travel datum, and a transmission phase where a signal carrying the monitoring datum is transmitted onward.Filed by Hitachi Rail STS S.p.A., published 2023-08-17. Its scope is specific to damper-state inference via neural network, not bogie sensing generally.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US11731673B1 | Wheel-mounted sensor ring apparatus for determining anomalies associated with a railcar wheelset, or a railca… | 13 |
| 2 | US8276440B2 | Device for error monitoring of chassis components of rail vehicles | 7 |
| 3 | US11656156B1 | Axle-mounted sensor cuff apparatus for determining anomalies associated with a railcar wheelset, or a railcar… | 6 |
| 4 | US20250145188A1 | Method for determining anomalies associated with a railcar wheelset, or a railcar bogie assembly that the rai… | 1 |
| 5 | US12145639B2 | Method for determining anomalies associated with a railcar wheelset, or a railcar bogie assembly that the rai… | 1 |
| 6 | US20240109565A1 | Axle-mounted sensor cuff assembly and wheel-mounted sensor ring apparatus for determining anomalies associate… | 1 |
Citation counts are drawn from the searched corpus only and favour older, well-indexed filings over recent ones.
Publication numbers are shown where the record carries one (6 of 6 rows); clicking a row searches Eureka by that number.
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Browse MCP servers →What the filing pattern says about this field
With only 14 families to work from, each data point carries outsized weight. The signals below are drawn directly from the trend, IPC mix and citation data for this search string.
A burst, not a build-up
Filings sat at 0 in 2017, reached only 2 by 2022, then jumped to 6 in 2023. That shape points to a triggering event, likely a fleet mandate or a specific product launch, rather than a technology that has been steadily maturing for a decade.
Measurement hardware, not control software
Testing and measurement claims (G01M) appear in 11 of the 14 records, well ahead of traffic-control (B61L) and AI-computing (G06N) claims. Most of the protected ground here is sensing and measurement apparatus, not the decision layer that acts on the readings.
Sensor placement claims lead influence
The most-cited record in this corpus is a wheel-mounted sensor ring apparatus claim, followed by an axle-mounted sensor cuff design and a legacy chassis error-monitoring patent. Citation weight here reflects age and prior indexing as much as current relevance.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to railway bogie condition monitoring, with the prior art for and against each one.
Who is filing, and where the claim space is still open
Assignee activity in this dataset is recent and thin across the board: none of the tracked filers show filings in the latest year, and one shows a full year-over-year drop-off. That flat momentum, combined with only 14 families total, means no single filer has established durable dominance yet.
A shallow, contestable field
With every tracked assignee showing zero filings in the latest year, the ranking reflects a field still forming rather than one with an entrenched leader. Recent M&A-scale rail suppliers and small specialist filers sit close together in filing count.
Even active filers have gone quiet
NVH Tech LLC shows a full year-over-year drop to zero, and every other tracked assignee, including Alstom Transport Technologies, Knorr-Bremse Rail Vehicle Systems and Hitachi Rail STS, also shows zero filings in the latest year. That flatness suggests filing decisions here track specific product cycles rather than continuous R&D investment.
One hardware family anchors the citation graph
The wheel-mounted and axle-mounted sensor apparatus claims sit at the top of the citation table and appear to originate from the same filer lineage. That concentration of citation weight in physical sensor placement, rather than in software inference claims, is the clearest signal of where prior art risk currently sits.
| Assignee | Recent year | YoY |
|---|---|---|
| BALANCED ENG SOLUTION LLC | 0 | — |
| NVH TECH LLC | 0 | -100% |
| Alstom Transport Technologies | 0 | — |
| Knorr-Bremse Rail Vehicle Systems GmbH | 0 | — |
| Hitachi Rail STS S.p.A. | 0 | — |
Where to take this analysis next
The trend and IPC data point to specific follow-up work depending on whether you are clearing a filing or scoping a competitive response.
Clear the sensor-placement claims before filing hardware
The wheel-mounted and axle-mounted sensor apparatus claims carry the most citation weight in this corpus. Any onboard sensor hardware filing should be checked against these specifically before drafting mounting-point language.
Search these claims in EurekaScope a claim in the acceleration-plus-traffic-control gap
G01P and G06N records are almost absent from this dataset, and B61L traffic-control integration is thin. A claim combining acceleration-derived scoring with traffic-control signalling has room to be drafted broadly.
Draft and stress-test claims in EurekaTrack the next filing cycle, not the last one
Every tracked assignee shows zero filings in the latest year, and the overall trend already peaked in 2023. Watch for the next product-driven filing burst rather than assuming steady decline.
Set up monitoring in EurekaFrequently asked questions
This dataset contains 14 published patent families matching railway bogie condition monitoring within the searched IPC codes and date range. That is a small, tightly bounded corpus rather than a sprawling one, which means individual filings carry more weight in shaping the competitive picture. Coverage runs from 2015 through a partial 2026, so the true current-year count will rise as later filings publish.
Filing activity peaked in 2023 with 6 records, up from 2 in 2022, before easing off. Because publication typically lags filing by around 18 months, the drop shown for the most recent years is partly an artefact of that lag rather than a confirmed decline in real filing behaviour. Readers should treat the last one to two years of any trend chart as understated.
Testing and measurement claims under IPC class G01M are the most heavily represented, appearing in 11 of the 14 records, followed by railway auxiliary equipment (B61K, 8 records) and running gear and bogie structure (B61F, 6 records). Traffic control (B61L), velocity/acceleration sensing (G01P) and AI-model computing (G06N) are present but thin, each with four records or fewer. That concentration shows most filers are protecting sensing and measurement hardware rather than the control-system or AI-inference layer around it.
By citation count, the most influential records are wheel-mounted and axle-mounted sensor apparatus claims (US11731673B1 with 13 citations and US11656156B1 with 6), alongside an older chassis-component error-monitoring patent (US8276440B2 with 7 citations). High citation counts reflect an older, well-indexed record's influence within this searched corpus, not necessarily its current commercial dominance, so newer filings may be under-counted simply because they have had less time to accumulate citations.
The thinnest branches in this dataset are velocity/acceleration sensing (G01P, 1 record) and AI-model-based computing (G06N, 1 record), especially where they might combine with traffic-control data (B61L, 4 records) rather than pure onboard hardware. A claim that fuses acceleration-derived degradation scoring with traffic-control signalling to trigger maintenance ahead of a scheduled inspection sits largely clear of the dense wheel- and axle-mounted sensor claims that otherwise dominate the field.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.