Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis

Nam V. Nguyen; Hoang Van Truong; Hieu D. D.; Duy Khuong Nguyen; Quang Pham; Tung Kieu

cs.AIOct 13, 2026

Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis

Authors: Nam V. Nguyen, Hoang Van Truong, Hieu D. D., Duy Khuong Nguyen, Quang Pham, Tung Kieu

Organizations: FPT Corporation · Independent Researcher · Aalborg University

Abstract

We study industrial fault diagnosis as a classification task that assigns sensor time-series windows to predefined health states or fault types. Scarce labels and changing operating conditions make repeated training of specialized classifiers costly. Yet structures such as periodicity, trends, and transients can remain diagnostically informative and be examined through reusable analytical tools. LLM agents can select and interpret these tools, but fixed rules or training-derived guidance may not resolve each signal's ambiguity, while unconstrained interpretation may overstate weak or conflicting results. We introduce Agentic4PDM, a framework that separates adaptive investigation from control over evidence use. LLM agents compare each signal with labeled references from every candidate class, identify unresolved distinctions, and propose follow-up measurements. A deterministic software controller, called the host, enforces contract-checked gated adjudication (CCGA): it checks requests, requires commitment to a complete tool batch before results are observed, and assigns evidence roles based on tool checks against labeled references. These roles distinguish corroborative support from advisory information. The Judge Agent proposes a diagnosis, while the controller checks protocol compliance and records the investigation. Agentic4PDM supports training-free diagnosis using pretrained representations and labeled references without task-specific diagnostic models, and a standard setting that additionally investigates trained-model predictions. Experiments on selected bearing and motor diagnosis challenges and an unfiltered industrial test set supplied by our customer show gains in aggregate macro F1 over the evaluated diagnostic and agentic baselines. Ablations further support the value of the governed workflow.

Figures & tables

Figure 1: Agentic4PDM workflow. The Host constructs and freezes class-balanced comparative evidence before the Judge begins sample-specific investigation. The Judge proposes follow-up measurements or a diagnosis, while the Host governs which measurements may run and how returned evidence may influence the decision. The Host then publishes the reported class, marked as admitted or not admitted, together with the record of the checks behind it.

Figure 2: Performance comparison between Agentic4PDM and baselines from three model families agentic methods, time-series foundation models, and deep-learning models across three evaluation settings: (a) GRS, (b) BFH, and (c) the customer test set (Bel). M1.1R, M2S, C2, and C2S denote Moirai 1.1 R Large, Moirai 2 Small, Chronos 2, and Chronos 2 Small, respectively; XGB denotes XGBoost.

Figure 4: Observed plans across agentic methods on PU and MCC5. Bar segments show the frequency of each plan, the right number indicates the total number of distinct plans.

	Enabled mechanisms					Performance (%)
Variant	CB retrieval	Analyst reports	Request gate	Packet & influence gate	Finali- zation	ACC $\uparrow$	F1-M $\uparrow$
A1 Full workflow	✓	✓	✓	✓	✓	47.24	38.54
A2 Global top- $K$	✗	✓	✓	✓	✓	37.20	29.89
A3 No Analyst reports	✓	✗	✓	✓	✓	46.35	37.72
A4 Ungoverned	✓	✓	✗	✗	✓	36.31	30.53
A5 No request gate	✓	✓	✗	✓	✓	39.96	33.35

Table 1: Ablation of the main components in Agentic4PDM on the PU data under GRS sampling.

Figure 5: Mean recorded time and total tokens per query for ZARA, FETA, and standard Agentic4PDM . Here, k denotes 1,000 tokens. Marker shapes identify methods; dashed lines guide the eye

Appendix figures & tables27 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Table 2 — Table 2: Performance comparison of Agentic4PDM against deep-learning, time-series foundation-model, and agentic baselines across three evaluation settings: BFH and GRS on the public benchmarks (UO, PU, and MCC5), and the full customer test set (Bel). ACC and F1-M denote accuracy and macro-F1, respectively; AVG is the unweighted mean across public datasets. Bold indicates the best results in each column.

Group	Method	Baseline-Failure Hard (BFH)						Geometry-Stratified Response-Stability (GRS)						AVG
		UO		PU		MCC5		UO		PU		MCC5		BFH		GRS
		ACC	F1-M	ACC	F1-M	ACC	F1-M	ACC	F1-M	ACC	F1-M	ACC	F1-M	ACC	F1-M	ACC	F1-M
	ZARA [ 39 ]	45.00	39.30	39.00	40.31	35.00	24.90	63.43	65.26	41.94	43.23	47.66	44.22	39.67	34.84	51.01	50.90
Agent	FETA [ 55 ]	34.00	32.32	36.00	33.84	17.00	17.65	53.73	53.46	35.21	30.79	38.02	36.90	29.00	27.94	42.32	40.38
Ours	Agentic4PDM (TF)	63.00	62.12	44.00	42.30	42.00	40.73	81.52	81.40	47.68	40.78	50.69	50.14	49.67	48.38	59.96	57.44

Appendix

Table 3: Performance comparison of training-free (TF) Agentic4PDM against ZARA and FETA across two public benchmarks, BFH and GRS, on UO, PU, and MCC5. ACC and F1-M denote accuracy and macro-F1, respectively; AVG is the unweighted mean across datasets. TF uses no fitted-model predictions. Bold indicates the best results in each column, including ties.

Figure 6: Variation across three executions of standard Agentic4PDM , ZARA, and FETA on BFH and GRS. For each method, dataset, and metric, scores are centered on their mean across the three executions. The dots therefore show deviations from that mean in percentage points, with runs 1–3 ordered from left to right within each group. Shaded bands span

\pm 1

sample standard deviation (

n=3

); the corresponding standard deviation appears below each group. AVG denotes the unweighted mean of the three dataset scores in each execution. BFH and GRS use different vertical scales.

Method	MCC5	PU	UO
Flat source plurality	48.26	28.45	85.00
Agentic4PDM	54.09	37.90	87.38

Appendix

Table 4: Macro-F1 (F1-M, %) of Flat source plurality and Agentic4PDM on the same covered samples in each dataset. Bold marks the higher score.

Table 10

Tool (selected on) / method	ToolCard / scoring	MCC5	PU	UO	Mean
Windowed profile (MCC5)	quant_windowed_profile_windows_16_amplitude_l1	36.15	35.22	30.17	33.85
Crossing-run statistics (PU)	quant_crossing_run_statistics_q50_js	22.89	37.94	16.73	25.85
Distribution KS (UO)	quant_distribution_ks_raw	31.78	32.78	60.02	41.53
Agentic4PDM	Saved class	52.93	38.54	86.12	59.20

Appendix

Table 7: Macro-F1 (%) of the dataset-winning tools among the 25 selectable defaults and the full Agentic4PDM in the updated GRS audit. Agentic4PDM uses saved-class scoring. Bold marks the highest displayed score in each column.

Method	Time (s)	Input (k)	Output (k)	Total (k)	LLM calls
FETA	1,078.59	68.05	4.62	72.67	8.0
ZARA	98.20	5.11	2.72	7.83	4.0
Agentic4PDM (standard)	1,645.79	89.88	12.48	102.36	17.4

Appendix

Table 8: Mean recorded resource use per query sample. Token columns are in thousands (k); time is in seconds. Recorded timer scopes differ across methods, as described below.

Figure 10: Diagnostic macro-F1 versus estimated Qwen3.6-27B API cost per query at Alibaba Cloud’s Singapore/International list prices. Costs follow Eq. 6 , using separate input and output token rates. Each method’s mean cost estimate is reused across GRS, BFH, and Bel. These values estimate token-based API charges rather than observed API bills or total deployment costs.

Figure 11: Accuracy of FETA, ZARA, and Agentic4PDM across the displayed PU operating conditions and MCC5 motor speeds.

Dataset	Classes	Train	Fault generation / setting	Operating conditions	Rate (Hz)
PU [ 36 ]	3	150	Real bearing damage from accelerated lifetime tests	Condition 0: 900 rpm, 0.7 Nm, 1000 N. Conditions 1–3: 1500 rpm with torque/radial-load pairs 0.1 Nm/1000 N, 0.7 Nm/400 N, and 0.7 Nm/1000 N.	64,000
UO [ 25 ]	3	150	Inner-/outer-race defect specimens; fabrication method not specified in the source description	Time-varying speed: A, increasing; B, decreasing; C, increasing then decreasing; D, decreasing then increasing.	200,000
MCC5 [ 8 ]	4	200	Laboratory motor measurements with rotor, electrical, and bearing fault configurations	Frozen scored pool: 20 Nm at 1000, 2000, and 3000 rpm (Conditions 0–2).	12,800
Bel	2	20	Bearing vibration supplied by our customer; binary normal/abnormal labels, with damage type unspecified	-	–

Appendix

Table 9: Acquisition characteristics and retained-task statistics for PU, UO, MCC5, and Bel. Bel is evaluated separately from BFH and GRS; a dash indicates an unreported sampling rate.

(a) Bearing diagnosis
Dataset	Challenge	Total $N$	Healthy	Inner-race	Outer-race
PU	BFH	100	34	33	33
PU	GRS	906	205	487	214
UO	BFH	100	34	33	33
UO	GRS	763	197	221	345

Appendix

Table 10: Test-set sizes and per-class support for the frozen BFH/GRS manifests and the complete Bel test set. Each class count is a number of samples; class counts sum to the total

N

in each row.

Model / backbone	Prediction / reasoning approach
(a) Deep learning Evaluated baselines
CNN-TCC [ 18 ] QCNN [ 41 ] WDCNN [ 66 ]	Neural classifiers
(b) Foundation models Evaluated baselines
Moirai 1.1 R Large [ 59 ] Moirai 2 Small [ 42 ] Chronos-2 [ 3 ] Chronos-2 Small [ 3 ]	Fixed representations with KNN, SVM, or XGBoost (12 combinations), following MOMENT’s representation-plus-SVM evaluation [ 22 ] , StarEmbed’s fixed-embedding evaluation [ 37 ] , and the broader embedding comparison [ 27 ] .
(c) Agentic methods Evaluated baselines
FETA [ 55 ]	Training-set channel ranking, DTW exemplar retrieval, and channel-level LLM reasoning with confidence-based aggregation.

Appendix

Table 11: Baseline inventory grouped by model family. Each foundation-model backbone is paired with KNN, SVM, and XGBoost.

Protocol item	Setting
Labeled reference pool	50 samples per class: 150 each for PU and UO (3 classes), and 200 for MCC5 (4 classes)
Query sample	2048 observations, non-overlapping, using all available dataset channels

Appendix

Table 12: Shared labeled-reference and query-sample settings for the evaluated methods; BFH/GRS manifest sizes and per-class support are reported in Table 10 .

Component	Setting	Value
(a) Language model
	Model and access	Qwen3.6-27B vision-language model, served locally through an OpenAI-compatible endpoint; the same model serves every agent role
	Decoding	Temperature 0, top- $p$ 1, top- $k$ $-1$ , presence penalty 1.5, seed 0
	Extended thinking	Enabled for the analysis and synthesis roles, 512-token budget
	Output limit	38,048 tokens per request
	Images	At most 10 per request, longest side 1400 px

Appendix

Table 13: Main Moirai 1.1 R Large Agentic4PDM configuration. One setting applies to standard and TF on both challenges and all three datasets; TF omits fitted-model outputs. Appendix E specifies the Chronos-2 Small substitution.

Optimizer				Schedule		Architecture
Algorithm	$\beta_{1}$	$\beta_{2}$	Learning rate	Epochs	Batch size	Bearing (3 channels)	Motor (8 channels)	Common
Adam	0.9	0.95	$10^{-3}$	100	128	First convolution: kernel 16, stride 2	First convolution: kernel 8, stride 1	64 final channels; dropout 0.35

Appendix

Table 14: Training settings shared by the deep-learning baselines (CNN-TCC, QCNN, and WDCNN).

Group	Setting	Value
(a) Foundation-model representations
	Backbones	Moirai 1.1 R Large, Moirai 2 Small, Chronos-2, Chronos-2 Small, all frozen
	Embedding	Mean pooling, per-dimension feature scaling, context length 2048, patch size 32, batch size 100
(b) Classical heads on those representations
	KNN	$k=5$ , uniform weights
	SVM	RBF kernel, $C=1.0$ , $\gamma=\text{scale}$

Appendix

Table 15: Training settings of the foundation-model baselines and their classical heads.

System	Method-specific workflow	Retrieval and prompting	Common configuration	Reported comparison
FETA [ 55 ]	Channel-level reasoning: ranks channels, compares retrieved exemplars, and aggregates per-channel judgments with confidence scores.	Channel ranking from the labeled reference bank; DTW exemplar retrieval; a per-channel comparison prompt; and deterministic confidence aggregation.	Qwen3.6-27B; deterministic decoding; frozen manifests; class-balanced reference bank; common resource limits.	Evaluated on the common BFH/GRS task, split, manifests, reference data, and scoring procedure.
ZARA [ 39 ]	Feature-guided reasoning: combines pairwise feature knowledge with hierarchical selection, candidate pruning, and decision synthesis.	Frozen-encoder, class-wise retrieval; pairwise feature-importance knowledge; feature-selection and decision prompts; and candidate pruning.	Qwen3.6-27B; deterministic decoding; frozen manifests; class-balanced reference bank; common resource limits.	Evaluated on the common BFH/GRS task, split, manifests, reference data, and scoring procedure.

Appendix

Table 16: Implementation settings for FETA and ZARA in the BFH and GRS evaluations. The table summarizes their method-specific workflows under common evaluation conditions.

Figure 12: GRS construction: (1) allocate geometry clusters within class–condition strata; (2) group frozen-model responses into stability tiers; (3) retain one observed representative per nonempty tier. Bars show response variability

I_{s}

; counts show cluster allocations

g_{s}

(illustrative total

G{=}250

). Stability is measured within each model across samples, not between models.

Symbol	Meaning
Inputs and reference retrieval
$x^{\star}$	Query multivariate time-series sample supplied for diagnosis.
$x^{\mathrm{prev}}$	Immediate predecessor of the query sample, available only through one-step temporal context.
$T,C$	Numbers of observations and sensor channels in one sample, respectively.
$\mathcal{Y},Y,y$	Diagnosis-label set, its number of classes, and a generic candidate class.
$\mathcal{D}_{\mathrm{ref}},N$	Labeled training set (the reference pool) and its number of samples.

Appendix

Table 17: Summary of main notation for the Agentic4PDM protocol and inference path.

#	Property	What it describes
1	amplitude_distribution_match	How the signal’s amplitude values are spread, i.e., the shape of their histogram.
2	severity_or_offset_distribution_shift	Whether the amplitude values are shifted, widened, or offset as a whole.
3	global_summary_match	Whole-sample summary statistics such as level, spread, and peakiness.
4	global_severity_or_impulsiveness	How strong and how spiky the signal is over the whole sample.
5	local_ordering_dynamics	The order in which neighbouring values rise and fall.
6	scale_invariant_local_shape	Local rise-and-fall patterns, regardless of amplitude and time scale.

Appendix

Table 18: Registered signal properties. Each property names an aspect of the signal that may remain unresolved; none refers to a class.

ToolCard	vibration_1 (high)	phase_current_1 (high)	phase_current_2 (medium)	Winning class	Step 1
Fractal scaling	0	0	2	Class 0	Usable
Local extrema	2	2	0	Class 2	Usable
Autocorrelation	0	2	1	None	Excluded

Appendix

Table 19: Step 1 on the round-1 packets of sample 107665: per-channel winners and the resulting winning class.

Sample	ToolCard	Class-0 control	Class-1 control	Class-2 control	$g_{\mathrm{ref}}(\tau)$	Role
106619	Autocorrelation	0	1	2	1	Corroborative
107665	Fractal scaling	0	none	none	0	Advisory

Appendix

Table 20: Reference preflight records: the class named by the ToolCard for each control, and the resulting role.

Action	Purpose and constrained request interface
compare_normal compare_abnormal	Re-check the normal-region overlay or the abnormal pairwise evidence. Request task_objective and judge_feedback ; the abnormal action may additionally bound target_classes .
view_image	Load one previously supplied figure for next-turn close inspection. Request image_path .
run_sandbox	Optional same-transform, full-sample comparative measurement. Request task_objective , sandbox_request , and metric_spec ; configurations that isolate the registered quantitative library disable this action.
select_quantitative_tools	Declare the unresolved properties $\Phi_{t}$ ; the Host returns the offer $\mathcal{S}_{t}$ from the 25 default ToolCards (Declare and Offer, Section 3.5.1 ).
run_quantitative_tools	Commit a batch $\mathcal{T}_{t}$ from the offer; the Host checks the request conditions and then runs it (Commit and Execute, Section 3.5.1 ).
finalize	Propose one terminal class decision. It must be standalone and include the Host-required fault-screen contract.

Appendix

Table 21: All seven general tool actions available to the Judge .

Family	Registered purpose
savgol	Light polynomial denoising that preserves local waveform shape.
median	Suppress isolated impulses without averaging their amplitude into neighbors.
ewm	Track a smooth causal-style local level with bounded lag.
median_mean_ewm	Extract a slow robust trend from mixed impulsive and broadband noise.
lowpass	Remove high-frequency content while avoiding phase shift.

Appendix

Table 22: Complete Stage-2 smoothing-family registry.

Tier	Assigned when
high	The channel is a primary discriminator: it shows transient bursts or impulses, a clear envelope change, a region marked as deviant, or class-separating structure whose removal would change the class ranking.
medium	The channel is useful but secondary: it gives operating context, such as load or phase-current behavior, or a weak deviation that supports but cannot settle the class. This is the default tier for a kept channel.
low	The channel carries weak but real class information and serves only as tie-break support.
removed	The channel shows no visible class information.

Appendix

Table 23: Channel priority tiers assigned by the CSAgent .

ID	Selectable ToolCard	Fixed configuration	Measurement purpose
T01	quant_autocorrelation_lags_128_pearson	max_lag=128; similarity=pearson	Compare periodic recurrence across lags without requiring cycle-phase alignment.
T02	quant_autoregressive_coefficients_order_8_l1	order=8; similarity=l1	Compare short-memory linear dynamics using regularized autoregressive coefficients.
T03	quant_cepstral_shape_quefrency_128_pearson	max_quefrency=128; similarity=pearson	Compare repeated spectral spacing over normalized quefrency bins.
T04	quant_crossing_run_statistics_q50_js	similarity=js; threshold_quantile=0.5	Compare threshold crossings, run lengths, and state persistence.
T05	quant_detrended_morphology_linear_residual_pearson	representation=linear_residual; similarity=pearson	Compare local residual shape after removing a declared slow trend.
T06	quant_distribution_ks_raw	kind=ks; standardize=false	Compare complete-sample amplitude distributions without requiring point alignment.

Appendix

Table 24: The 25 default quantitative ToolCards available to the agent. Each entry gives its exact identifier, fixed configuration, and measurement purpose. Eligibility remains subject to the input, requested properties, question scope, and budget.

Appendix	Protocol event shown
N	Agent selection of transform families and channel priorities, followed by Host parameter tuning, deterministic segmentation, and channel guards.
N	Frozen NAAgent and ABAgent comparisons that reject the healthy class but leave the two fault classes live.
N	Uncertainty declaration, score-blind ToolCard offer, pre-result batch commitment, and Host-controlled execution.
N	Packet provenance checks, reference preflight, influence assignment, and the round report.
N	Judge proposal, Host recheck of the earlier records, and the admitted Class 2 record.

Appendix

Table 25: Roadmap for the complete workflow trace.

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In so-called "Industry 4.0," integrated sensors, analytics tools, and other digital technologies supplement industrial workflows, potentiating enhanced visibility and automation. Prognostics and health management (PHM) and natural language processing (NLP) practitioners alike have explored the application of large language models (LLMs) to industrial asset management, but many prior works' focus on exam-like benchmarks has limited their assessment of LLMs' ability to automate practical operations. Herein, we introduce CoffeeBench, a fault diagnostics task for LLM agents, using a simulated appliance. The LLMs we evaluate struggle to consistently identify faults from the sensor information alone, but the best-performing ones can iteratively refine the fault-option space to eventually obtain a successful diagnosis. Our subsequent analyses, therefore, focus on: differentiating models by diagnostic efficiency (RQ1); examining the lagging performance of the three worst models (RQ2); and identifying viable heuristics for selective prediction (RQ3), including that initial diagnostic uncertainty and LLM-judged soundness of reasoning are high-signal predictors of diagnostic success. In addition to our benchmark results (Section 3.1), we provide practical insights for improving the performance of the open-weight models on CoffeeBench, such as by explicitly tracking episode state on their behalf to reduce timeouts (Section 3.2) or applying a deliberation loop to refine diagnostic hypotheses (Section 3.3).

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