NTSB CAROL · Event
Event CEN22LA326
Registry · N457PA
FAA Aircraft Registry record.
Make / Model
HELICOPTERES GUIMBAL CABRI G2
Year of manufacture
2019 · 3 years old at event
TCDS
R00005RD · HELICOPTERES GUIMBAL
Engine
LYCOMING O-360-J2A (145 hp)
Seats / Engines
2 seats · 1 engine
Last airworthiness date
20190709
ADS-B equipped
Yes — Mode-S A58D63
Registrant of record
IDEAL AVIATION ILLINOIS LLC
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
Loss of tail rotor effectiveness resulting in an in-flight loss of control.
Factual narrative
The flight instructor and student pilot reported that the purpose of the flight was to practice off-airport landings. On the fourth and final landing, they set up a steep approach with the student pilot on the flight controls. The helicopter was above the effective translational lift (ETL) airspeed as they descended through 100 ft above ground level (agl) in a stabilized descent. The helicopter then started to yaw left and the flight instructor took the controls. As the spin rate increased, the flight instructor’s efforts to regain control were not successful. He then realized the helicopter was spinning out of control. He lowered the collective and the spin rate slowed as the helicopter contacted the ground. The flight instructor shut down the engine and both pilots exited the helicopter. The helicopter’s fuselage sustained substantial damage. The flight instructor reported that there were no preaccident failures or malfunctions with the helicopter that would have precluded normal operations. Source: NTSB Aviation Accident Database Retrieved: 2026-02-12
NTSB Findings
Hierarchical cause / factor breakdown from the FAA bulk avdata database. Each finding tagged C (Cause) or F (Factor).
- — Personnel issues-Task performance-Use of equip/info-Aircraft control-Flight crew
- — Personnel issues-Action/decision-Info processing/decision-Identification/recognition-Instructor/check pilot
- — Personnel issues-Action/decision-Action-Delayed action-Instructor/check pilot
Verbatim from NTSB's published report. Source file
NTSB_2022_CEN22LA326.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
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Related research
What the literature says.
Academic papers and agency reports matching this event's aircraft type or causal vocabulary (loss of control). Sourced from NASA NTRS, NTSB Safety Studies, FAA CAMI, AOPA Air Safety Institute, Embry-Riddle Scholarly Commons, arXiv, and the Semantic Scholar academic graph.
- Embry-Riddle Scholarly Commons 2025 · Journal article (JAAER)
A Scoping Review of Aviation Loss of Control Inflight Research
Loss of control – inflight (LOC-I) contributes to aircraft accidents at unacceptably high rates. Significant industry efforts and research have aimed to improve LOC-I prevention, detection, and recove…
- SKYbrary (Eurocontrol) 2024 · SKYbrary article
Loss of Control In-Flight (LOC-I) — SKYbrary Knowledge Base
SKYbrary comprehensive knowledge-base entry on Loss of Control In-Flight — definitions, contributing factors, accident case studies (Air France 447, Colgan 3407), and prevention strategies.
- NTSB Aircraft Accident Reports 2022 · Accident report
Loss of Control on Takeoff in Icing Conditions — Citation 560XL
Cessna Citation 560XL fatal takeoff icing accident, March 2018. Investigation of a Citation 560XL loss-of-control takeoff accident in icing conditions.
- Semantic Scholar 2021 · Article (Aviation)
ANALYSIS OF GENERAL AVIATION FIXED-WING AIRCRAFT ACCIDENTS INVOLVING INFLIGHT LOSS OF CONTROL USING A STATE-BASED APPROACH
Inflight loss of control (LOC-I) is a significant cause of General Aviation (GA) fixed-wing aircraft accidents. The United States National Transportation Safety Board’s database provides a rich source…
- NASA NTRS 2021 · Presentation
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
- NASA NTRS 2021 · Conference Paper
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
Browse the full corpus — academia portal ↗