CEN22LA068
2021-12-09 · McKinney, Texas, United States · Serious · 1 aircraft · Status: Completed
Airport TKI
Current FAA registration · N666JS
- 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
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
FAA avdata. C = Cause, 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 ↗.
Search this event elsewhere
External sources are reported, not agency: signal that something happened, not fact about what happened.
- TallyAero Live Wire Aviation press
- NTSB CAROL Agency ↗
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Related research
Matched on aircraft type or causal vocabulary (loss of control). All research papers
- 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.
- Semantic Scholar 2024 · Article (International Conference on Networking and Services) Risk Assessment of Loss of Control In-Flight Trajectories for Urban Air Mobility Safety
In Urban Air Mobility (UAM), maintaining a minimum separation between aircraft is a safety requirement, which becomes challenging amidst congested air traffic and off-nominal conditions, such as Loss …
- 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.