Business Impact
The Problem
Manufacturing facility experienced $3.2M in annual losses from unplanned equipment failures. Existing threshold-based alerts produced 60% false positives, causing alert fatigue and missed critical failures.
- • 12 major equipment failures per year
- • Average $267K cost per failure incident
- • 60% false positive rate with rule-based systems
The Solution
Deployed ML-based anomaly detection using Isolation Forest with ensemble methods, processing real-time sensor streams to predict failures 24-48 hours in advance.
- • 40% reduction in unplanned downtime
- • $1.3M annual savings in maintenance costs
- • 94.5% accuracy with only 2.1% false positives
Real-Time Sensor Monitoring
Live demo of the anomaly detection system
Total Readings
0
Anomalies Detected
0
Detection Accuracy
94.5%
Avg Processing Time
12.0ms
Temperature (°C)
Vibration (g)
Pressure (PSI)
Recent Anomalies
No anomalies detected yet. Start the stream to begin monitoring.
Technical Implementation
ML Pipeline
- •Isolation Forest: Primary unsupervised anomaly detector trained on 6 months of normal operation data
- •Random Forest Classifier: Secondary model for anomaly type classification and root cause identification
- •Statistical Methods: Z-score and IQR-based outlier detection as ensemble members
- •Feature Engineering: Rolling statistics, FFT for vibration analysis, time-based features
Infrastructure
- •Apache Spark Streaming: Distributed processing of 10K+ events/second across 8-node cluster
- •Apache Kafka: Message queue for reliable sensor data ingestion with exactly-once semantics
- •AWS S3 + Glue: Data lake for historical analysis and model retraining
- •Kubernetes: Container orchestration with auto-scaling based on stream volume
Technology Stack
Apache SparkApache KafkaPySparkIsolation ForestRandom ForestAWS S3AWS GlueDockerKubernetesPythonscikit-learnGrafanaPrometheus