NTSB CAROL · Event
Event CEN22LA068
Registry · N666JS
FAA Aircraft Registry record.
Make / Model
CHR INTERNATIONAL INC SAFARI 400
Engine
LYCOMING IO-360 SERIES (200 hp)
Seats / Engines
2 seats · 1 engine
Last airworthiness date
20140714
ADS-B equipped
Yes — Mode-S A8CB5C
Registrant of record
BROUGHTON TIM
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
The pilot did not remove the cyclic control lock during preflight, which resulted in a loss of control during the initial hover.
Factual narrative
The pilot reported that he drove from his home to the airport not expecting to fly the helicopter due to high winds, however, after arrival at the hangar the winds were lighter than the forecast winds, so he conducted a preflight for a local flight. As the pilot raised the collective for takeoff and the helicopter climbed into an initial hover, the helicopter rolled to the left. The pilot tried to counter the roll but was unable because the cyclic control lock was still in place. The helicopter continued to roll left and impacted the ramp and a post impact fire ensued. The pilot successfully egressed the helicopter but suffered serious burn injuries. The helicopter sustained substantial damage to the main rotor system and the cabin. The pilot reported that there were no preaccident mechanical malfunctions or failures that would have precluded normal operation. 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-Inspection-Preflight inspection-Pilot
- — Aircraft-Aircraft handling/service-Maintenance/inspections-(general)-Inadequate inspection
- — Aircraft-Aircraft oper/perf/capability-Performance/control parameters-Lateral/bank control-Attain/maintain not possible
Verbatim from NTSB's published report. Source file
NTSB_2021_CEN22LA068.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 ↗