MCSA analyses the electrical current drawn by a motor to detect mechanical and electrical faults — such as bearing wear, broken rotor bars, or winding issues — without needing to open the equipment or attach vibration sensors directly to it. It is attractive for predictive maintenance because current sensors are cheaper to install and maintain than vibration or acoustic sensors, and can often be added at the motor control cabinet rather than on the machine itself. In this patent set, MCSA-related filings sit heavily in the G01R electric and magnetic measurement class, which covers 61.3% of the 483 records, indicating that most of the claimed innovation is in the sensing and signal-processing layer rather than in the control response.

The assignee ranking is concentrated at the top: the leading assignee holds 74 records, and the top 5 assignees combined account for 209 records, or 43.3% of the 483 records in scope. After the top five, the count per assignee drops quickly — from 20 records at fifth place to 8 at tenth — before spreading into a long tail of companies and institutions with only a handful of filings each. This pattern is typical of an established industrial-automation niche where a small number of incumbents built early portfolios and later entrants file narrower, more targeted claims.

Filing counts rose from 13 in 2017 to a peak of 34 in 2025, and the most reliable comparison — between the two complete years 2021 and 2024 — shows 31 filings in each, a flat 0% change rather than growth or decline. Because patent publication typically lags actual filing by around 18 months, the apparent dip in 2025 and 2026 in raw counts is very likely an artefact of records not yet having published, not a real slowdown. Readers should treat the last one to two years of any filing trend chart as provisional and wait for the lag to clear before drawing conclusions about momentum.

US6199023B1, assigned to General Electric Company, claims a method for removing spurious signals from a motor current signature by modelling the motor electronically, comparing a predicted current against the measured current, and subtracting the difference before running the corrected signal through analysis. It is one of the most heavily cited records in this set, meaning many later filings reference it as prior art, which is a signal of foundational influence on the field's vocabulary and technique set. A new signal-correction method that relies on the same model-then-subtract approach for spurious signal removal should be checked closely against its claim scope; approaches based on different correction mechanisms, such as pure frequency-domain filtering without a motor model, are more likely to sit outside it.

The IPC composition shows a large gap between measurement-related classes and adjacent application areas: G01R measurement claims cover 61.3% of the 483 records, while G06N AI-based computing methods reach only 9.5% and G01H vibration/sound measurement sits at 5.0%. This suggests that fusing current-signature data with other sensing modalities, or applying newer AI model architectures specifically to fault classification rather than to general signal processing, remains comparatively under-claimed. Protective circuit arrangements (H02H) also sit at a modest 6.2% of records, pointing to an opening around automated fault-response and isolation methods that build directly on a detected current signature.