Kevlar & SupportGuru
SupportGuru
I’ve been mapping out a failure analysis for a high‑speed motor, and I could use your knack for spotting hidden threats. Think a risk matrix that also covers sabotage scenarios would make it airtight. What’s your take?
Kevlar
Use a simple 3x3 grid: high, medium, low likelihood versus high, medium, low impact.
For sabotage, flag any component that can be tampered with: electrical contacts, coolant lines, firmware, and the motor housing.
Add a row in the matrix for “intentional damage” and rate it high impact if the motor fails suddenly.
Mitigation: harden the firmware with cryptographic checks, seal critical connectors, install tamper‑evident seals, and monitor for abnormal voltage spikes or temperature rises.
Keep the matrix in a secure log, update it after each test run, and run a quick walk‑through before any high‑speed operation.
SupportGuru
Looks solid—just a couple of tweaks. Add a quick visual check of the tamper‑evident seals after each test run and consider a redundant path for the coolant line, just in case that gets cut. Keep the risk matrix in a version‑controlled log so you can track changes over time. If you need a quick script to flag abnormal spikes or seal status, let me know.
Kevlar
Nice add‑ons. Seal check after every run keeps the evidence chain tight. Dual coolant paths give a back‑up if a line’s cut. Version control on the matrix is a no‑lose—easy audit trail. I’ve got a Python snippet that watches the pressure and temperature sensors, flags spikes over threshold, and logs seal status. Let me know if you need it and I'll drop it into your repo.
SupportGuru
Sounds good. Just double‑check that the thresholds in your script account for the motor’s transient spikes during startup. Also make sure the log writes to a write‑once medium so tampering with the data isn’t an option. Drop the snippet in the repo, and I’ll give it a quick run-through.
Kevlar
Got it. Here’s a quick Python script that reads the pressure, temp and seal‑status sensors, applies a startup‑spike filter, logs to an append‑only file, and writes the log entry in a single write so it can’t be chopped. Save it as coolant_watch.py and push it to the repo.
```python
import time
import json
import os
from datetime import datetime
# sensor read functions – replace with your actual I/O calls
def read_pressure():
return 1.2 # bar
def read_temperature():
return 85.0 # °C
def read_seal_status():
return "OK" # or "Breach"
# thresholds
STARTUP_PEAK_PRESSURE = 1.5 # bar
STARTUP_PEAK_TEMP = 90.0 # °C
PERM_PRESSURE = 1.0
PERM_TEMP = 80.0
LOG_FILE = "/var/log/coolant_watch.log"
def log_event(event):
entry = {
"timestamp": datetime.utcnow().isoformat(),
"event": event
}
# write in one go – no truncation
with open(LOG_FILE, "a") as f:
f.write(json.dumps(entry) + "\n")
def main():
# initial startup window
startup = True
for _ in range(10):
p = read_pressure()
t = read_temperature()
s = read_seal_status()
if startup:
if p > STARTUP_PEAK_PRESSURE or t > STARTUP_PEAK_TEMP:
log_event(f"startup spike detected: pressure={p} bar, temp={t} °C, seal={s}")
else:
if p > PERM_PRESSURE or t > PERM_TEMP:
log_event(f"abnormal spike: pressure={p} bar, temp={t} °C, seal={s}")
if s != "OK":
log_event(f"seal breach detected: {s}")
time.sleep(1)
startup = False
if __name__ == "__main__":
main()
```
Make sure the log directory is owned by a non‑privileged user and that the file is set to immutable once you’re confident the script is stable. That should keep the data tamper‑proof and give you the alerts you need.