> ## Documentation Index > Fetch the complete documentation index at: https://docs.appliedaifoundation.org/llms.txt > Use this file to discover all available pages before exploring further. # Auxiliary Engine Performance > End-to-end pipeline that captures auxiliary engine telemetry, scores blackout risk, classifies SFOC and overhaul exposure, and produces a senior expert review. ## Executive summary Auxiliary engines do not power the vessel — they power everything else on it. A single AE failure with no standby is a blackout. A blackout in port is a delay; a blackout at sea is a casualty. The job of a Technical Superintendent reviewing AEs is therefore not just "is the engine healthy" but "is the **fleet of generators** healthy enough to ride through any one of them failing". The **Auxiliary Engine Performance** pipeline answers both questions on a rolling basis. It captures every monitored AE parameter from the ERP's monthly performance reports, evaluates engine availability and load-sharing posture, scores blackout risk, and produces a senior review whenever a vessel's redundancy degrades or an engine drifts toward overhaul. ## Architecture ``` ┌─────────────────────────────────────────┐ │ ERP telemetry · Engine register · PMS │ └─────────────────────┬───────────────────┘ │ monthly reports + specs ▼ ┌─────────────────────────────────────────┐ │ Stage 1 — Collection │ │ Per-engine telemetry, hours, alarms │ └─────────────────────┬───────────────────┘ │ structured telemetry ▼ ┌─────────────────────────────────────────┐ │ Stage 2 — Analysis │ │ • Engine availability & redundancy │ │ • Load-sharing posture │ │ • SFOC variance per engine │ │ • Six-month parameter rollup │ │ • Overhaul timing │ │ • Blackout-risk score │ └─────────────────────┬───────────────────┘ │ verdict + scores ▼ ┌─────────────────────────────────────────┐ │ Stage 3 — Expert review │ │ Auto-escalate to TSI when triggered │ └─────────────────────────────────────────┘ ``` *** ## Stage 1 — Data collection ### Where the data comes from | Source | What it provides | | -------------------------- | ------------------------------------------------------------------------------ | | **Vessel ERP** | AE register, running hours, status (running / standby / fault), alarms history | | **Forms (AE Performance)** | Monthly per-engine data filed by Chief Engineer | | **PMS counter feed** | Hours since overhaul per engine — drives overhaul timing | The AE collector pulls the latest performance entry per engine from the telemetry feed. The same six-month rolling window used for ME reports applies — every monthly performance report, parameter by parameter, per AE unit. ### What gets captured | Domain | Data | | ---------------- | ------------------------------------------------------------------------------------- | | Engine state | Status (running / standby / fault / maintenance), running hours, hours since overhaul | | Load | Load percentage, power output, exporting / importing | | Per-cylinder | Pmax, exhaust temperature, FW inlet/outlet temp | | Aspiration | Boost pressure, exhaust back-pressure | | Fuel | SFOC actual, fuel grade in use | | Power management | Number of engines online, blackout-risk inputs | | Alarms | Active alarms with severity at sample time | ### Output A single timestamped record per vessel covering every AE plus the power-management context. *** ## Stage 2 — Analysis ### 2.1 Engine availability and redundancy Each AE is bucketed by status. The fleet-of-generators view is the **availability tier**: | Running | Standby | Tier | Risk | | ------- | ------- | ----------------------- | -------- | | 1 | 2+ | Adequate redundancy | LOW | | 1 | 1 | Minimum redundancy | MEDIUM | | 1 | 0 | No redundancy | HIGH | | 2+ | 0 | Load sharing, no backup | MEDIUM | | 0 | any | Blackout — terminal | CRITICAL | Utilization is tracked too: if the same engine always runs, wear is uneven; if standby rotation is happening, the fleet stays balanced. Standby rotation cadence is a leading indicator of how the engineers actually operate the plant. ### 2.2 SFOC variance Per-engine SFOC variance follows the same definition as the [main engine](/skills/me-performance): $$ \text{Variance \%} = \frac{\text{SFOC}_\text{actual} - \text{SFOC}_\text{design}(P)}{\text{SFOC}_\text{design}(P)} \times 100 $$ Sustained variance above 8% is flagged. Above 10% triggers escalation. AEs typically run at narrower load bands than ME, so variance shows up faster. ### 2.3 Daily fuel-cost impact Variance is converted to dollars per day so the review carries commercial weight: $$ \Delta C_\text{day} = \frac{(\text{SFOC}_\text{actual} - \text{SFOC}_\text{design}) \cdot P \cdot 24}{10^{6}} \quad \text{[MT/day]} $$ $$ \text{Cost}_\text{day} = \Delta C_\text{day} \cdot \text{Price}_\text{fuel} \quad [\$] $$ For a 1.2 MW AE running at 75% load with 5% variance and a $700/MT MGO price, that's roughly **$120/day of waste\*\*, or \$44k/year per engine — usually enough to justify an injector replacement. ### 2.4 Six-month parameter rollup The same dashboard format used for ME applies — every monitored parameter per engine per month. The grid logic, condensed: ```python theme={"system"} # For each AE (AE1 / AE2 / …) build a six-month grid keyed by parameter for ae_number in sorted(engines): for parameter_key in PARAMETERS: # PMAX, EXHAUSTTEMP, … main_row = [f"{ae_number} {parameter_key}"] for month_date, month_label in zip(six_months_dates, six_months_labels): performance = performance_data.get(month_date) if performance == "No Data Available": current = (month_date.month == datetime.utcnow().month and month_date.year == datetime.utcnow().year) main_row.append("Report yet to be submitted" if current else "Missing Report") continue value = performance["data"].get(f"{parameter_key}AVERAGE") main_row.append(round(value, 2) if value is not None else "No Data Available") ``` Two states matter in the gaps: **report yet to be submitted** (current month, expected) versus **missing report** (past month, compliance issue). The analyzer surfaces both. ### 2.5 Cylinder uniformity Same logic as the main engine — cylinder deviation against the engine mean: $$ \Delta T_i = T_i - \bar{T}, \quad \bar{T} = \frac{1}{N}\sum_{i=1}^{N} T_i $$ | Severity | Threshold | | -------- | -------------------------------------------------- | | OK | $\lvert \Delta T_i \rvert \leq 30\,°\text{C}$ | | WARNING | $30 < \lvert \Delta T_i \rvert \leq 50\,°\text{C}$ | | CRITICAL | $\lvert \Delta T_i \rvert > 50\,°\text{C}$ | ### 2.6 Overhaul timing $$ H_\text{remaining} = H_\text{interval} - (H_\text{current} - H_\text{last\_overhaul}) $$ Tiered identically to ME (see [main engine page](/skills/me-performance)). The difference is that an AE overhaul typically degrades the redundancy tier above — booking maintenance forces a tier check on the rest of the fleet. ### 2.7 Blackout-risk score The composite that drives the senior verdict: $$ R_\text{blackout} = w_1 N_\text{fault} + w_2 (1 - C_\text{margin}) + w_3 R_\text{redundancy} + w_4 N_\text{overdue} $$ where: * $N_\text{fault}$ — count of engines in FAULT * $C_\text{margin}$ — capacity margin = $\frac{\text{available kW} - \text{load kW}}{\text{available kW}}$ * $R_\text{redundancy}$ — redundancy tier multiplier (LOW=0, MEDIUM=1, HIGH=2) * $N_\text{overdue}$ — overhauls overdue The composite maps to LOW / MEDIUM / HIGH / CRITICAL. HIGH or CRITICAL escalates immediately. *** ## Worked example **Vessel**: `MV POSUN` (IMO 9388340), 3 × MAN 7L21/31 generators. After a refresh: | Engine | Status | Hours | SFOC variance | Notes | | ------ | ------- | ------ | ------------- | ------------------------------------- | | AE1 | RUNNING | 12,400 | +6% | Cylinder 4 hot (38 °C above mean) | | AE2 | STANDBY | 8,200 | — | OK, available | | AE3 | FAULT | 11,900 | — | Tripped on low LO pressure 6 days ago | State: 1 running, 1 standby, 1 in fault → redundancy tier **HIGH**, capacity margin tight if AE1 fails. Composite blackout-risk: **HIGH**. The pipeline: 1. Tags the run with `escalation_required: true` 2. Updates the case to `awaiting-tsi-review`, priority CRITICAL 3. Sends an A2A message to the TSI inbox with engine evidence and recommended actions **Recommended actions**: 1. Investigate AE3 LO-pressure trip root cause — restore service before next port departure. 2. Replace AE1 cylinder 4 fuel injector at next port — variance plus cylinder hot signature. 3. Confirm AE2 standby readiness drill within 24h while AE3 is out. *** ## Output deliverables * **Executive summary** — overall AE status, redundancy tier, blackout risk * **Per-engine table** — status, load, hours, SFOC variance, alarms * **Cylinder uniformity** — per-cylinder deviation per engine * **Six-month rollup** — every monitored parameter per engine per month * **Power-management assessment** — load profile, capacity margin, blackout-risk score * **Overhaul plan** — urgent / upcoming, port-call coordination * **Cost impact** — variance translated to daily and annual fuel waste * **Recommendations** — prioritised actions with timeline * **Escalation decision** — auto-routed to TSI when triggered *** ## Escalation triggers | Trigger | Severity | | ---------------------------------------- | -------- | | Any engine in FAULT | CRITICAL | | Blackout-risk score = HIGH or CRITICAL | CRITICAL | | Cylinder deviation above 50 °C | CRITICAL | | Overhaul overdue or under 500 hours away | CRITICAL | | Multiple engines showing WARNING | HIGH | | SFOC variance above 8% on any engine | HIGH | | Load-sharing failure | HIGH | | Exhaust temp above 420 °C | CRITICAL | *** ## Why script-driven Redundancy tiers, SFOC variance, and blackout-risk weighting all live in deterministic Python. The reviewer interprets the result; the verdict is reproducible. A review where the redundancy tier shifts from MEDIUM to HIGH between two consecutive runs is a real event in the data, not a model decision. *** ## References Performance analysis suite — AE monthly rollup and engine specifications & running hours. Same analysis pattern applied to the main propulsion plant. AE LO trends often confirm cylinder findings. Fuel quality is a common cause of variance and cylinder asymmetry.