Time Series Anomaly Detection for Vessel Telemetry
Time series anomaly detection is the practice of flagging readings or stretches of a signal that depart from its expected behavior, such as a bearing temperature drifting away from its usual pattern. For vessel operators the method matters less than the data underneath it: the same algorithm behaves very differently on clean, comparable telemetry than on signals named and scaled differently by each manufacturer.
What Are the Main Approaches to Time Series Anomaly Detection?
The approaches range from simple thresholds to deep learning. The evaluation behind the VLDB 2022 benchmark, published as Anomaly Detection in Time Series: A Comprehensive Evaluation, collected 158 publications on time series anomaly detection algorithms and describes approaches running from simple outlier detection through statistical analysis and signal processing to deep learning. Its authors also state that selecting a suitable algorithm for a given task is extremely difficult, because every approach has its own strengths and weaknesses.
| Approach | How it works | Where it fits |
|---|---|---|
| Statistical thresholds | Flags values outside a fixed or rolling range | Single sensors with well-understood limits |
| Signal processing | Looks at frequency, trend and seasonality changes | Rotating machinery and cyclic loads |
| Unsupervised machine learning | Learns normal behavior from unlabeled history and scores deviations | Equipment with little labeled failure history |
| Deep learning | Models long, multivariate patterns with neural networks | Many correlated signals and complex patterns |
The survey on deep learning for time series anomaly detection on arXiv offers a taxonomy of deep models and discusses the advantages and limitations of each category. It also notes that anomalies can signal events such as production faults or system defects, which is the reason an operator cares about them at all.
Which Kinds of Anomalies Show Up in Vessel Telemetry?
Choosing a method starts with naming the anomaly you expect. Three shapes cover most machinery cases.
| Anomaly type | Vessel example | Detection note |
|---|---|---|
| Point anomaly | One exhaust temperature spike | Thresholds often catch it, but sensor glitches also look like this |
| Contextual anomaly | A normal-looking load that is wrong for the current speed or sea state | Needs other signals as context |
| Collective anomaly | A slow, sustained drift across a stretch of readings | Needs windowed or sequence-aware methods |
Contextual and collective anomalies are where simple thresholds fall short, and they are often the early warnings that precede a failure.
Why Does Anomaly Detection Struggle Across Mixed OEM Equipment?
A detector learns what normal looks like for a signal. If one manufacturer calls a signal one thing, another uses a different name and unit, and a third logs it at a different rate, a model cannot compare engines or generators across the fleet. Each asset ends up with its own bespoke model, and nothing learned on one sister vessel transfers to the next.
That is a data problem before it is a modeling problem. Our guide to how to normalize OEM telemetry across manufacturers covers the naming and unit work, and the idea of a unified data schema for industrial AI describes the structure that keeps those signals comparable.
How Do You Set Up Anomaly Detection for a Fleet?
- Name the failure you want to catch early, such as drift in a cooling circuit, and list the signals that would show it.
- Map those signals to common identifiers and units across every OEM platform involved.
- Establish a baseline per equipment type and operating condition, not just per sensor.
- Start with a simple method and keep its alerts as a reference before adding learned models.
- Attach context to each alert: recent maintenance, warranty status and procedures, so someone can act on it.
- Review false alarms with technicians and feed their judgment back into the baseline.
The fifth step is the one most projects skip. An alert with no maintenance history behind it sends a technician searching several systems, which is why a pattern flagged on one asset rarely reaches the next. Linking alerts to records is also what makes root cause analysis for vessel maintenance possible afterward.
Where Does SailPlan Fit?
SailPlan works on the data layer, not on a single detection algorithm. It builds a unified, machine-readable model that normalizes OEM telemetry, maintenance logs, procedures, warranty and vendor terms and other records into one schema, so any authorized tool can query it without a custom integration for each source. Machine-readable here means normalized identifiers, typed relationships and a queryable structure, which is more than digitized files. Anomaly detection across mixed OEM equipment is one of the uses it describes, alongside root-cause tracing and true cost per operating hour. Its work is starting with maritime.
Because the model is not tied to any AI vendor, the translation work is done once, and a new detection model or dashboard can be added later without paying a recurring integration tax. Operators who want to see how this would be built from their existing systems can request a demo, and a team member follows up within one business day. Earlier SailPlan emissions monitoring was acquired by Verret Marine Consulting, so questions about that platform belong with Verret Marine.
FAQ
Is unsupervised anomaly detection enough for machinery data?
Often it is the practical starting point, because labeled failures are rare. It learns normal behavior and scores departures, but it cannot say whether a departure is harmful. That judgment needs context such as operating mode and maintenance history.
Can large language models detect time series anomalies?
They are better suited to explaining and routing alerts than to scoring raw signals. A language model that can query normalized records can summarize an alert alongside related procedures, while a dedicated detector still does the signal scoring. The supplied sources do not benchmark language models, so treat claims of superiority with caution.
What is a simple anomaly detection example on a ship?
A generator's coolant temperature creeps upward over several days while load stays steady. No single reading crosses an alarm limit, but a windowed method comparing it with its usual behavior at that load flags the drift.
Does graph anomaly detection apply here?
It can, where relationships matter, for example how equipment, personnel, maintenance and procurement records connect. That requires typed relationships between records, which is what a unified schema supplies. Without them, the graph has nothing reliable to analyze.
Which matters more, the algorithm or the data?
The benchmark authors found algorithm selection hard precisely because results vary with datasets and anomaly types. On a fleet, comparable signals and linked records decide whether any of those algorithms can be tested fairly against your own equipment.
Related resources
- How to Normalize OEM Telemetry Across Manufacturers
- One Schema for Telemetry, Procedures, Financials and Institutional Knowledge: A Unified Data Schema for Industrial AI
- Root Cause Analysis for Vessel Maintenance: Why It Depends on Unified Data
- GCMD’s Loo: Decarbonization Requires Real-Time Emissions Measurement — SailPlan
- Optimizing Vessel Performance with SailPlan: A Next-Generation CEMS Solution — SailPlan
- SailPlan featured in Seaports Magazine: Ports and Surrounding Communities Deserve Real-Time Measurement of Air Pollution and Emissions — SailPlan
- Industrial Data Platform vs Data Lake: What's the Difference, and How Do You Choose for Vessel Operations Data?
SailPlan builds the machine-readable data model that makes every AI tool in your stack actually work. Request a demo to see it in action.