Tools

Tools: How to get individual SMART data from a TerraMaster DAS (and build a failure forecaster around it) (2026)

2026-04-18 0 views admin
Tools: How to get individual SMART data from a TerraMaster DAS (and build a failure forecaster around it) (2026)

The fix: JMicron pass-through

Why this matters

Building a forecaster

How the forecast works

Thresholds

Config for a TerraMaster D5-300

Getting started If you have a TerraMaster D5-300 — or any DAS with a JMicron JMB576 bridge chip — you've probably hit this wall: One result. For the whole enclosure. Not the 5 individual WD Red Pros inside it. Most SMART monitoring guides stop here and tell you to use the vendor app. I went a different way. The JMB576 chip supports SMART pass-through via a specific smartctl flag: The N in jmb39x,N maps directly to the physical slot. Run this and you get full per-disk SMART output — temperature, reallocated sectors, CRC errors, pending sectors, everything. Tested on: TerraMaster D5-300, Debian 12.5, smartctl 7.3. With per-disk SMART data, you can actually monitor disk health instead of just hoping the enclosure is OK. But raw SMART numbers aren't enough — what you really want is trends. A single udma_crc_error_count = 1 on a 4-year-old disk is probably fine. udma_crc_error_count going from 1 to 3 to 7 to 12 over 60 days is not fine — it's a cable or backplane issue that will get worse. I built Argus around this discovery. It collects SMART attributes every 6 hours, builds a 180-day rolling history, and runs a linear regression over the last 30 days to forecast when each attribute will hit its critical threshold. The output looks like this: Slot 5 is trending. Not critical yet — but I know about it 142 days before it becomes a problem. The core is a simple linear regression over (days_elapsed, attribute_value) pairs: Nothing fancy. But with 6h collection frequency and 30 days of history, it gives you 5+ data points per attribute — enough to catch real trends while ignoring one-off noise. Argus uses Backblaze-calibrated thresholds rather than vendor defaults. A few notes: The installer auto-discovers your disks via smartctl --scan, walks you through the config, and sets up cron jobs for collection and alerting. Has anyone else found JMicron pass-through working on other enclosure brands? Curious how far jmb39x,N generalises beyond TerraMaster. Templates let you quickly answer FAQs or store snippets for re-use. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse

Command

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$ smartctl -a /dev/sdb ... SMART overall-health self-assessment test result: PASSED ... $ smartctl -a /dev/sdb ... SMART overall-health self-assessment test result: PASSED ... $ smartctl -a /dev/sdb ... SMART overall-health self-assessment test result: PASSED ... smartctl -a -d jmb39x,0 /dev/sdb # slot 1 smartctl -a -d jmb39x,1 /dev/sdb # slot 2 smartctl -a -d jmb39x,2 /dev/sdb # slot 3 smartctl -a -d jmb39x,3 /dev/sdb # slot 4 smartctl -a -d jmb39x,4 /dev/sdb # slot 5 smartctl -a -d jmb39x,0 /dev/sdb # slot 1 smartctl -a -d jmb39x,1 /dev/sdb # slot 2 smartctl -a -d jmb39x,2 /dev/sdb # slot 3 smartctl -a -d jmb39x,3 /dev/sdb # slot 4 smartctl -a -d jmb39x,4 /dev/sdb # slot 5 smartctl -a -d jmb39x,0 /dev/sdb # slot 1 smartctl -a -d jmb39x,1 /dev/sdb # slot 2 smartctl -a -d jmb39x,2 /dev/sdb # slot 3 smartctl -a -d jmb39x,3 /dev/sdb # slot 4 smartctl -a -d jmb39x,4 /dev/sdb # slot 5 👁️ Argus SMART Analysis — 2026-04-17T09:00:00+00:00 Samples: 28 (forecast window: 30d) Overall: WARNING ✅ ssd-system (SanDisk Ultra II 960GB) health=95/100 -weight: 500;">status=OK ✅ das-slot1 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK ✅ das-slot2 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK ✅ das-slot3 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK ✅ das-slot4 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK 🟡 das-slot5 (WD Red Pro 8TB) health=70/100 -weight: 500;">status=WARNING 🟡 udma_crc_error_count=1 ≥ WARN (5) 📈 udma_crc_error_count: 1→100 forecast in 142d 👁️ Argus SMART Analysis — 2026-04-17T09:00:00+00:00 Samples: 28 (forecast window: 30d) Overall: WARNING ✅ ssd-system (SanDisk Ultra II 960GB) health=95/100 -weight: 500;">status=OK ✅ das-slot1 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK ✅ das-slot2 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK ✅ das-slot3 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK ✅ das-slot4 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK 🟡 das-slot5 (WD Red Pro 8TB) health=70/100 -weight: 500;">status=WARNING 🟡 udma_crc_error_count=1 ≥ WARN (5) 📈 udma_crc_error_count: 1→100 forecast in 142d 👁️ Argus SMART Analysis — 2026-04-17T09:00:00+00:00 Samples: 28 (forecast window: 30d) Overall: WARNING ✅ ssd-system (SanDisk Ultra II 960GB) health=95/100 -weight: 500;">status=OK ✅ das-slot1 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK ✅ das-slot2 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK ✅ das-slot3 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK ✅ das-slot4 (WD Red Pro 8TB) health=100/100 -weight: 500;">status=OK 🟡 das-slot5 (WD Red Pro 8TB) health=70/100 -weight: 500;">status=WARNING 🟡 udma_crc_error_count=1 ≥ WARN (5) 📈 udma_crc_error_count: 1→100 forecast in 142d def linear_forecast(points: list, target: float) -> float | None: xs = [p[0] for p in points] ys = [p[1] for p in points] n = len(points) mean_x = sum(xs) / n mean_y = sum(ys) / n num = sum((x - mean_x) * (y - mean_y) for x, y in zip(xs, ys)) den = sum((x - mean_x) ** 2 for x in xs) if den == 0 or (slope := num / den) <= 0: return None x_target = (target - (mean_y - slope * mean_x)) / slope days_until = x_target - max(xs) return round(days_until, 1) if 0 < days_until <= 180 else None def linear_forecast(points: list, target: float) -> float | None: xs = [p[0] for p in points] ys = [p[1] for p in points] n = len(points) mean_x = sum(xs) / n mean_y = sum(ys) / n num = sum((x - mean_x) * (y - mean_y) for x, y in zip(xs, ys)) den = sum((x - mean_x) ** 2 for x in xs) if den == 0 or (slope := num / den) <= 0: return None x_target = (target - (mean_y - slope * mean_x)) / slope days_until = x_target - max(xs) return round(days_until, 1) if 0 < days_until <= 180 else None def linear_forecast(points: list, target: float) -> float | None: xs = [p[0] for p in points] ys = [p[1] for p in points] n = len(points) mean_x = sum(xs) / n mean_y = sum(ys) / n num = sum((x - mean_x) * (y - mean_y) for x, y in zip(xs, ys)) den = sum((x - mean_x) ** 2 for x in xs) if den == 0 or (slope := num / den) <= 0: return None x_target = (target - (mean_y - slope * mean_x)) / slope days_until = x_target - max(xs) return round(days_until, 1) if 0 < days_until <= 180 else None # /opt/argus/config/argus.conf [argus] history_file = /var/lib/argus/argus-history.json retention_days = 180 [ntfy] url = http://your-ntfy:8080 topic = argus-disk [disk:ssd-system] device = /dev/sda type = sat class = ssd [disk:das-slot1] device = /dev/sdb type = jmb39x,0 class = hdd [disk:das-slot2] device = /dev/sdb type = jmb39x,1 class = hdd [disk:das-slot3] device = /dev/sdb type = jmb39x,2 class = hdd [disk:das-slot4] device = /dev/sdb type = jmb39x,3 class = hdd [disk:das-slot5] device = /dev/sdb type = jmb39x,4 class = hdd # /opt/argus/config/argus.conf [argus] history_file = /var/lib/argus/argus-history.json retention_days = 180 [ntfy] url = http://your-ntfy:8080 topic = argus-disk [disk:ssd-system] device = /dev/sda type = sat class = ssd [disk:das-slot1] device = /dev/sdb type = jmb39x,0 class = hdd [disk:das-slot2] device = /dev/sdb type = jmb39x,1 class = hdd [disk:das-slot3] device = /dev/sdb type = jmb39x,2 class = hdd [disk:das-slot4] device = /dev/sdb type = jmb39x,3 class = hdd [disk:das-slot5] device = /dev/sdb type = jmb39x,4 class = hdd # /opt/argus/config/argus.conf [argus] history_file = /var/lib/argus/argus-history.json retention_days = 180 [ntfy] url = http://your-ntfy:8080 topic = argus-disk [disk:ssd-system] device = /dev/sda type = sat class = ssd [disk:das-slot1] device = /dev/sdb type = jmb39x,0 class = hdd [disk:das-slot2] device = /dev/sdb type = jmb39x,1 class = hdd [disk:das-slot3] device = /dev/sdb type = jmb39x,2 class = hdd [disk:das-slot4] device = /dev/sdb type = jmb39x,3 class = hdd [disk:das-slot5] device = /dev/sdb type = jmb39x,4 class = hdd -weight: 500;">git clone https://github.com/pdegidio/argus-disk.-weight: 500;">git cd argus-disk bash -weight: 500;">install.sh -weight: 500;">git clone https://github.com/pdegidio/argus-disk.-weight: 500;">git cd argus-disk bash -weight: 500;">install.sh -weight: 500;">git clone https://github.com/pdegidio/argus-disk.-weight: 500;">git cd argus-disk bash -weight: 500;">install.sh - seek_error_rate is excluded — Seagate packs seek totals in the upper 32 bits, making the raw value meaningless for cross-vendor comparison. Backblaze doesn't use it either. - udma_crc_error_count warns at 5, not 1 — a single historical CRC on a multi-year disk is physiological. Growth is what matters, and the forecast captures it. - Temperature anomaly uses z-score over 10+ samples, not a fixed threshold — so a disk that normally runs at 28°C will alert at 36°C, while one that normally runs at 38°C won't.

🏷️ Tags

toolsutilitiessecurity toolsindividualsmartterramasterbuildfailureforecasteraround

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