Gearbox Condition Monitoring Patents: Who Leads, Trends 2026
- 80.4% concentration. The top 5 of 16 ranked assignees hold 45 of the 56 records in scope, leaving a long tail of single- and double-filing entrants.
- Filing has flattened, not grown. The 2021→2024 span shows 0% growth (1 to 1), against a 2017 peak of 12 filings — activity has not returned to that level.
- Testing and control dominate, AI is a minority branch. G01M testing/structure-balance claims cover 48.2% of records; G06N AI-model claims cover only 12.5%, pointing to open ground in learning-based diagnostics.
Filing growth compares 2021 (1 records) with 2024 (1) — a three-year span. 2024 is the most recent year we treat as complete: publication lags filing by roughly 18 months, so 2025 onwards are still filling in and any growth rate that ends there would understate the field. Top-5 share is the combined record count of the five largest assignees divided by all 56 records in scope (CR5), not by the ranked leaders only.
What this landscape covers
Gearbox condition monitoring sits at the intersection of mechanical sensing and diagnostic software: patents in this set combine claims on vibration and acoustic sensing (sideband analysis, gear mesh frequency, envelope spectrum, acoustic emission) with claims on oil debris sensing and on the software layer that turns sensor streams into a trend, a threshold or a remaining-useful-life estimate. The search string was built to capture that pairing directly, so the 56 records in scope are ones that assert both a sensing or signal-processing route and a diagnostic or prognostic output — not generic gearbox hardware and not generic machine-learning claims in isolation.
Filing activity peaked in 2017 and has not returned to that level since; publication lags filing by roughly 18 months, so the most recent one to two years in any chart will look thinner than they eventually turn out to be. Receiving-office data shows the United States and the EPO carrying the bulk of filings, with India, WIPO/PCT, Canada and Germany forming a second tier.
Filing trend and technology composition
Two views of the same 56-record dataset: filings by year, and the IPC subclasses those filings claim into. Because a single record can carry several IPC classes, the composition shares add up to more than 100% of records — that is expected and is how the chart below should be read.
Filing trend, 2017–2026
Filings ran at 12 in 2017, the peak year so far, then settled into a low, flat pattern; the 2021→2024 window is flat at 0% growth (1 filing to 1 filing). 2026 is a partial year at the data cut-off and will fill in as later publications land.
IPC subclass composition
G01M (testing machine and structure balance) is the largest single class at 48.2% of the 56 records, followed by G05B (control and regulating systems) at 30.4% and G06F (electric digital data processing) at 19.6%. Aviation-specific claims under B64D and B64F appear in a meaningful minority of records, reflecting aircraft-gearbox use cases; AI-model claims under G06N sit at 12.5%, the smallest of the listed classes relative to the testing and control branches.
Shares are the percentage of the 56 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Gearbox Condition Monitoring with Eureka
This page is one run against one query. Ask Eureka your own question about gearbox condition monitoring and every answer comes back with the patent numbers behind it.
Try EurekaRepresentative filing and most-cited records
Predicting remaining useful life of gearbox equipment from sensor data with a machine learning model
A life prediction system for industrial mechanical power transmission equipment is provided. The system includes a life prediction computing device, the life prediction computing device including at least one processor in communication with at least one memory device, and the at least one processor programmed to receive data of a gearbox measured by one or more sensors, predict remaining useful lifetime of the gearbox based on the received data by using a machine learning model, and output the predicted life of the gearbox.Filed by Dodge Industrial, Inc. in 2023, this record sits squarely in the sensor-to-RUL pipeline that defines the modern end of this landscape: it does not claim a specific sensor type, it claims the prediction architecture that sits downstream of whatever sensor supplies the data.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20140008307A1 | Two-stage microfluidic device for acoustic particle manipulation and methods of separation | 169 |
| 2 | US20070198215A1 | Method, system, and computer program product for performing prognosis and asset management services | 75 |
| 3 | US7328128B2 | Method, system, and computer program product for performing prognosis and asset management services | 60 |
| 4 | WO2012135663A2 | Two-stage microfluidic device for acoustic particle manipulation and methods of separation | 37 |
| 5 | US20200309641A1 | Sensor system for monitoring a vehicle axle and for discriminating between a plurality of axle failure modes | 22 |
| 6 | US20160236794A1 | Self-referencing sensors for aircraft monitoring | 21 |
| 7 | EP3242118A1 | Sensor system for monitoring a vehicle AXLE and for discriminating between a plurality of AXLE failure modes | 20 |
| 8 | US20120073364A1 | Sideband energy ratio method for gear mesh fault detection | 18 |
| 9 | WO2017191313A1 | Sensor system for monitoring a vehicle AXLE and for discriminating between a plurality of AXLE failure modes | 15 |
| 10 | US9821310B2 | Two-stage microfluidic device for acoustic particle manipulation and methods of separation | 15 |
Citation counts favour older records simply because they have had more time to be cited within the searched corpus; treat them as a signal of influence, not of which claims matter most today.
Each row carries its publication number; clicking a row searches Eureka by that number.
Put your own technology through the same analysis
Eureka on the web
When you want the answer in the next five minutes.
The agent works the prompt against patents and technical literature, citing every source.
Run your analysis now →MCP server & REST API
When it has to run inside your own pipeline.
Patent search, landscape analysis and assignee resolution as MCP tools. Drop them into any agent framework, or call REST directly.
Browse MCP servers →What the data means for a filing decision
Three read-outs from the dataset that matter more than the raw counts on their own.
The field is concentrated but not closed
Five assignees hold 45 of the 56 records in scope. That is a dense claim position at the top, but the remaining 16 ranked filers each hold a handful of records or fewer — a long tail that suggests specific sub-claims are still open even where the broad approach is occupied.
Activity has stabilised, not accelerated
The 2017 peak of 12 filings has not been matched since; the 2021-to-2024 window is flat at one filing to one filing. Momentum data on individual ranked assignees shows several leaders with zero filings in the latest year, consistent with a mature, low-velocity filing pattern rather than a growth phase.
Sensing and testing claims outweigh AI claims by a wide margin
Testing and structure-balance claims (G01M) appear in nearly half of records, while AI-model claims (G06N) appear in about one in eight. Software-and-control claims (G05B, G06F) are a stronger secondary presence than AI specifically, suggesting the diagnostic-algorithm layer is less crowded than the sensing layer beneath it.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to gearbox condition monitoring, with the prior art for and against each one.
Assignee landscape and co-filing patterns
The ranking covers 16 companies in total across the 56 records in scope — this is the complete ranked set the dataset returns, not a top-50 cut. A small number of co-assignee pairs recur, suggesting joint filing programmes rather than incidental overlap.
One filer sits well ahead of the field
The top-ranked assignee holds 15 of the 56 records, more than double the fifth-place count of 3. That gap is the clearest signal in the ranking: a single filer has built a broad position while the rest of the field files in smaller, more targeted clusters.
A small cluster of repeat co-filers
Ten co-assignee pairs appear in the dataset, with three pairs tied at the strongest link strength recorded. That pattern points to a small set of organisations filing jointly on a recurring basis rather than one-off collaborations.
Even leaders show no recent-year filings
Momentum data for the leading assignees shows zero filings in the latest year across several of them. Combined with the flat 2021–2024 trend, this suggests the field's most active period has already passed through the pipeline, though publication lag means very recent filings may not yet be visible.
| Assignee | Recent year | YoY |
|---|---|---|
| General Electric Co | 0 | — |
| Bell Helicopter Textron Inc | 0 | — |
| Dana Italia S.r.l. | 0 | — |
| ABB (Schweiz) AG | 0 | — |
| ZHEJIANG | 0 | — |
| JO MYEONG CHAN | 0 | — |
| GULDIKEN RASIM OYTUN | 0 | — |
| National Institute of Advanced Industrial Science and Technology | 0 | — |
Where to take this analysis
The dataset points to specific follow-up questions rather than a single conclusion.
Check freedom-to-operate against the leader's claim set
With one assignee holding 15 of 56 records, any new filing in the sensing-to-diagnosis pipeline should be checked against that portfolio specifically before broader prior art searches.
Explore assignee portfolios in EurekaTest claim language in the under-claimed sub-areas
AI-model RUL estimation and sensor-fusion approaches show lower class coverage than core vibration testing; drafting around a specific failure mode or sensor combination may find more open ground.
Run a white-space search in EurekaRevisit the trend once 2025–2026 publications land
The flat 2021–2024 filing rate and thin 2026 count both understate current activity due to publication lag; a re-check in a year will show whether the flat pattern held.
Track this landscape in EurekaCommon questions about this landscape
The ranking covers 16 assignees across the 56 records in scope, and it is led by a single company holding 15 records, well ahead of the fifth-ranked filer at 3 records. The top 5 assignees together hold 45 records, or 80.4% of the total, which means most of the documented claim space sits with a small group. Outside that group the ranking thins quickly into single- and double-record filers, so a freedom-to-operate check should weight the leader's portfolio most heavily.
Filing peaked in 2017 at 12 records and has not returned to that level since. The most recent complete comparison window, 2021 to 2024, shows 0% growth — one filing in 2021 and one in 2024. Because publication lags filing by roughly 18 months, the thin-looking 2025 and 2026 counts should not be read as a further decline; they simply have not finished publishing yet.
Testing and structure-balance claims under IPC class G01M are the largest single category, appearing in 48.2% of the 56 records, followed by control and regulating systems (G05B) at 30.4% and digital data processing (G06F) at 19.6%. Aircraft-specific equipment claims (B64D, B64F) also feature, reflecting aviation gearbox monitoring as a distinct use case. AI-model claims (G06N) are comparatively rare at 12.5%, suggesting the learning-based diagnostic layer is less occupied than the underlying sensing and testing methods.
US20230086049A1, filed by Dodge Industrial in 2023, claims a system that receives sensor data from a gearbox and uses a machine learning model to predict the gearbox's remaining useful life, then outputs that prediction. It is representative of the sensor-to-RUL pipeline that runs through much of this dataset: the claim sits downstream of the sensor type, so it can potentially cover RUL prediction regardless of whether the underlying signal is vibration, acoustic emission or oil debris. Anyone building a similar prediction layer should review its specific claim scope and file history rather than assume it only applies to one sensor modality.
The technology composition shows AI-model claims (G06N, 12.5% of records) and business/administrative data processing claims (G06Q, 12.5%) covering a smaller share of the dataset than core testing (G01M, 48.2%) and control systems (G05B, 30.4%). That gap suggests learning-based diagnostic algorithms, sensor-fusion approaches combining oil debris with acoustic emission, and failure-mode-specific remaining-useful-life models are less densely claimed than the underlying sensing hardware and signal-processing methods. A first claim in these areas would likely need to specify the failure mode, sensor combination or model architecture narrowly to clear the existing testing-focused prior art.
Research Gearbox Condition Monitoring in depth with Eureka
Go past this page: query the whole gearbox condition monitoring corpus yourself, in your own scope.
Every answer comes back with patent numbers you can open.
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.