Deployment-Oriented Causal Decision Stabilization for Fixed-FPR Constrained Streaming Industrial Acoustic Anomaly Detection in IIoT

Abstract

Industrial acoustic anomaly detection in long-duration streaming monitoring suffers from nonstationary noise, cross-machine domain shifts, and transient acoustic disturbances, leading to fluctuating anomaly scores and unstable alarm behavior. Existing methods mainly focus on improving backbone-level score generation, but direct thresholding under a fixed operating point may still cause alarm flickering, fragmented alarm events, delayed responses, and missed anomalies. To address these challenges, this article proposes a deployment-oriented causal backend decision framework for streaming industrial acoustic anomaly detection under fixed false-positive-rate constraints. The proposed framework treats backbone networks as anomaly-score generators and introduces an inference-stage decision layer to stabilize score-to-alarm conversion. Anomaly probabilities are transformed into the logit domain and processed by causal filters, including moving average (MA), exponential MA (EMA), exponentially weighted MA (EWMA), median filtering, adaptive Kalman filter (AKF), and recursive least square (RLS). A normal-only initialization strategy and fixed-FPR threshold calibration are further employed to support practical deployment without requiring abnormal calibration data. Experiments on the MIMII dataset, including leave-one-ID-out evaluation, cross-machine validation, logit-domain ablation, and edge-device benchmarking, demonstrate that the proposed framework improves MissRate, ToggleRate, AlarmSeg/h, and AvgLatency while introducing negligible inference overhead, validating its effectiveness for reliable Industrial Internet of Things (IIoTs) edge monitoring.